United States Air and Radiation EPA420-R-99-031 Environmental Protection December 1999 Agency 4vEPA Technical Support Document for the Tier 2/Gasoline Sulfur Ozone Modeling Analyses > Printed on Recycled Paper ------- EPA420-R-99-031 December 1999 for the Emissions Analysis and Monitoring Division Office of Air Quality Planning and Standards U.S. Environmental Protection Agency ------- Table of Contents I Introduction 1 II Ozone Modeling over the Eastern U.S 1 A. Episode Selection 1 1. Episodic Meteorological Conditions and Ozone Levels 2 2. General Representativeness of Episodic Ozone as Compared to Design Values 5 B. Domain and Grid Configuration 6 C. Meteorological Modeling 8 D. Development of Other UAM-V InputFiles 9 E. Model Performance Evaluation 9 1. Statistical Definitions 10 2. Evaluation Results 11 in Ozone Modeling over the Western U.S 12 A. Episode Selection 12 1. Episodic Meteorological Conditions and Ozone Levels 12 2. General Representativeness of Episodic Ozone as Compared to Design Values 13 B. Domain and Grid Configuration 14 C. Meteorological Modeling 16 D. Development of Other UAM-V Input Files 16 E. Model Performance Evaluation 17 IV Results of the Tier 2/Gasoline Sulfur Modeling for Ozone 18 A. Analysis of Need 18 B. Impacts of Tier 2/Sulfur Program on Ozone Levels 18 1. Methods for Quantifying Impacts 19 a. Definition of Areas for Analysis 19 b. Description of Ozone Metrics 19 2. Impacts on Ozone in 2007 and 2030 20 a. Impacts in 2007 20 b. Impacts in 2030 21 C. Additional Analyses to Support Responses to Comments 21 1. Local-scale Model Performance 21 2. Determination of Alternate Attainment Targets 23 3. Estimation of Attainment/Nonattainment using Relative Reduction Factors 24 V References 26 ------- I. Introduction This document describes the ozone modeling performed as part of the Tier 2/Sulfur final rulemaking. The ozone modeling was conducted to support several components of the rulemaking including (a) the determination of need for the Tier 2/Sulfur program, (b) the benefits/cost analysis, (c) an assessment of the expected impacts of the program on future ozone concentrations, and (d) the preparation of responses to comments on the proposed rulemaking. The modeling involved simulations of the Urban Airshed Model-Variable Grid (UAM-V), (SAI 1996) in a regional mode for two modeling domains (eastern U.S. and western U.S.) which together cover nearly all of the 48 contiguous States. Model runs were made for a 1996 Base Year and four future-year emissions scenarios: a 2007 Base Case, a 2007 Tier 2/Sulfur Control Case, a 2030 Base Case and a 2030 Control Case. These scenarios, along with the procedures followed to develop the emissions inventories for modeling, and the impacts of the control scenarios on emissions are described elsewhere (Pechan, 1999). For the eastern U.S., model simulations were made for all five emissions scenarios. However, since modeling for the West was intended mainly to support the benefit/cost analysis which was performed for 2030, only the 1996 base year and 2030 base case and control scenarios were modeled for the West. As described below, for both the East and West, emissions scenarios were modeled using meteorological conditions for several multi-day episodes when ambient measurements recorded high ozone concentrations. The remainder of this report includes a description of the modeling system, the modeling episodes, the base year model performance over the eastern and western U.S., and a discussion of the results of these simulations. II. Ozone Modeling over the Eastern United States A. Episode Selection There are several considerations involved in selecting episodes for an ozone modeling analysis (EPA, 1999). In general, the goal should be to model several differing sets of meteorological conditions leading to ambient ozone levels similar to an area's design value1. Ideally, the modeling time periods would be supported by large amounts of ambient data to be used in input development and model evaluation. The issue, in terms of regional modeling, is how to meet these episode selection goals over a large number of individual ozone non- attainment areas without having to model several entire ozone seasons (impossibly time consuming and resource-intensive). It is inevitable that the chosen episodes will feature observed ozone lower than the design value in some areas and greater than the design value in other areas. For the Tier 2/Sulfur analyses, we focused on the summer of 1995 for selecting the 1 Typically defined as the fourth-highest 1-hour daily maximum ozone observed over a three year period at a specific monitor. 1 ------- episodes to model in the East because 1995 is a recent time period for which we had model-ready meteorological inputs and the summer of 1995 contained one of the four episodes used by the Ozone Transport Assessment Group (OTAG) for modeling regional ozone over the eastern U.S. Based on a review of observed daily maximum ozone concentrations across the eastern U.S. during June through August, three episodes were selected for ozone modeling: June 12-24, July 5-15, and August 10-21. The start of each episode was picked to correspond to days with no ozone exceedances. Thirty episode days were modeled in all, not including the three ramp-up days used in each episode to minimize the effects of initial conditions. The meteorological conditions and ozone levels during each episode are described below. 1. Episodic Meteorological Conditions and Ozone Levels Warm temperatures, light winds, cloud-free skies, and stable boundary layers are some of the typical characteristics of ozone episodes. On a synoptic scale, these conditions usually result from a combination of high pressure aloft (500 millibars) and at the surface. At a smaller scale, the conditions that lead to local ozone exceedances can vary from location to location (based on factors such as wind direction, sea/lake breezes, etc.) The meteorological and resultant ozone patterns for the three Tier 2 modeling episodes are discussed in more detail below. June 12-24. 1995 The initial stages of this episode were fairly typical from the standpoint of regional meteorology. A 500-millibar ridge propagated into the eastern U.S. from the west. The ridge was associated with a surface high that migrated south from Canada. A cold front passed completely through the region by June 13 (Wednesday) allowing the modeling to start with a clean set of initial conditions. Maximum temperatures during the June 15 - 17-period were generally in the 80s and little precipitation was measured. By June 17, a strong (1028 mb) surface high was anchored over the region. The observed ozone fields in the early part of the episode were high (e.g., 125-130 ppb) only in locations such as Houston, Beaumont, and Lake Michigan. It was not until June 17 that concentrations exceeded 100 ppb over large parts of the domain (i.e., Midwest and Northeast Corridor). However, as the aloft pattern amplified, a cut off low developed over the southeastern U.S. On the 19th and 20th, cooler temperatures and occasional rain prevailed in the Southeast. This resulted in a temperature pattern that featured maximums of 90-100 degrees F over the northern tier of States and 75-85 degrees F in the south. Additionally, the strong cyclonic circulation around this low resulted in aloft flow from east to west over the mid-Atlantic and Ohio Valley States. Ozone continued to build throughout this period in the Northeast, peaking on the 19th and 20th with values greater than 125 ppb common from Washington, D.C. to Boston. ------- The last four days of the episode were relatively clean in the Northeast due to the combination of a "backdoor" cold front and the northward migration of the cut off low. Meanwhile ozone conducive conditions returned to the Texas Gulf Coast and Lake Michigan areas. The highest value over the entire summer of 1995 (210 ppb) was recorded near Houston on the 22nd. The episode came to an end on the 25th as a long-wave trough replaced the 500-mb ridge over the eastern U.S. Table II-1 shows a State-by-State listing of daily exceedance counts during the June 1995 Tier 2 episode. There were 85 exceedances of the ozone NAAQS during this period. The peak day of the episode was June 19. Texas had the most exceedances (28). Table II-l. Summary of exceedance days, by State/day, for the June 1995 Tier 2 episode. 6/12/95 6/13/95 6/14/95 6/15/95 6/16/95 6/17/95 6/18/95 6/19/95 6/20/95 6/21/95 6/22/95 6/23/95 6/24/95 AL AR CT 1 2 3 2 DE 3 2 DC 1 1 FL GA IL 4 IN 1 1 2 KY LA 2 2 ME MD 1 7 1 MA 2 MI 4 M 2 NH NJ 4 3 NY 2 NC OH 1 OK PA 8 RI 1 SC TN TX 1 1 1 1 3 7 7 4 3 VA 1 W WI 1 2 TOT 0 1 1 2 2 7 6 32 13 7 7 6 11 July 5-15. 1995 The mid-July episode, which covered most of the Ozone Transport Assessment Group (OTAG) July 1995 episode, is much easier to characterize from a meteorological perspective. A strong 500-mb ridge progressed from west to east across the eastern U.S. over the period. This feature was centered over Colorado on the 8th, over Kansas on the 11th, over Illinois on the 13th, and over Pennsylvania on the 15th. The ridge finally flattened out on the 16th allowing a surface cold front to clean out the northern portions of the domain and less stable conditions to prevail over the southern portions. Excessively hot temperatures accompanied the core of this strong ridge. Temperatures in the 90s and 100s were common throughout the episode. Rainfall was confined primarily to the coastal regions in the south and southeast. Wind speeds were moderate and the mean transport direction was southwest to northeast, especially over the northern half of the domain. ------- From the 8th through the 10th, the airmass over the eastern U.S. was gradually becoming hazy. Ozone hot spots occurred in urban areas like Houston, Dallas, and Atlanta. By the 11th, the area of regional haze (roughly defined as the area where peak ozone was greater than 75 ppb) had expanded to encompass most of the domain. On top of that "background," local contributions from urban emissions yielded ozone exceedances in places like Kansas City, St. Louis, Birmingham, Dallas, Memphis, Atlanta, Baton Rouge, Evansville, Louisville, Cincinnati, Chicago, Milwaukee, Columbus, and Baltimore/Washington on the 11th and 12th. July 13 and 14 marked the highest regional ozone levels of the summer as most sites, with the exception of those in the Southeast, exceeded 100 ppb. Almost all major metropolitan areas in the northern two-thirds of the domain measured values greater than 125 ppb on this day. For the 14th and 15th, most of the ozone problem shifted east and south due to both transport and the location of the aloft core of warm air. The Northeast Corridor, Charlotte, Greensboro, Birmingham, and Atlanta all had exceedances of the standard on this day. The episode ended abruptly on the 16th (Sunday) for most of the domain, although elevated ozone lingered over the southern regions into the early part of the next week. Table II-2 shows a State-by-State listing of daily exceedance counts during the July 1995 Tier 2 episode. There were 199 exceedances of the ozone NAAQS during this period. The peak day of the episode, in terms of exceedance monitors was July 14. Texas had the most exceedances (26). Table II-2. Summary of exceedance days, by State/day, for the July 1995 Tier 2 episode. 7/05/95 7/06/95 7/07/95 7/08/95 7/09/95 7/10/95 7/11/95 7/12/95 7/13/95 7/14/95 7/15/95 AL 1 1 1 AR 1 1 CT 5 7 3 DE 3 3 DC FL GA 1 4 3 IL 8 2 2 IN 1 1 2 2 KY 1 LA 1 1 ME 1 MD 5 2 4 10 MA 3 2 MI 7 6 1 M 1 3 4 6 NH NJ 1 7 6 NY 1 5 3 NC 3 OH 3 4 3 OK PA 1 3 5 RI 3 SC TN 1 1 1 TX 2 2 4 1 5 5 6 1 VA 4 W 1 WI 7 TOT 0 0 2 2 5 6 15 30 46 53 40 August 7-21. 1995 A one-day ozone event occurred over New England on August 10, and a separate one-day event occurred in the Lake Michigan region on the 12th. By the 14th, high pressure aloft and at the surface dominated the eastern half of the U.S. Temperatures ranged from 90 to 100 degrees F over most of the domain throughout this period. Ozone was highest over Georgia, Tennessee, ------- Kentucky, North Carolina, and Virginia during this period. Hurricane Felix brushed the East Coast from the 16th - 18th, but appeared to have little effect on ozone or ozone transport away from the immediate eastern seaboard. A weak cold front, draped across the Great Lakes over most of the episode, moved slowly southward over the eastern half of the Appalachians during the August 18-21 period. This front initiated precipitation that helped keep ozone concentrations low in the upper Midwest. The 18th featured high ozone across the South in cities such as: Atlanta, Charlotte, Birmingham, Augusta, as well as St. Louis. On the 19th and 20th, as the front slid further south, ozone air quality improved over this region as well. Only sites in Texas and Louisiana remain above 125 ppb. The 21st marked the fourth day that the same airmass has resided over the Northeast and it had become fairly polluted by that point. Table II-3 shows a State-by-State listing of daily exceedance2 counts during the August 1995 Tier 2 episode. There were 90 exceedances of the ozone NAAQS during this period. The peak day of the episode, in terms of exceedance monitors was August 21st. Texas had the most exceedances (15). Table II-3. Summary of exceedance days, by State/day, for the August 1995 Tier 2 episode. 8/07/95 8/08/95 8/09/95 8/10/95 8/11/95 8/12/95 8/13/95 8/14/95 8/15/95 8/16/95 8/17/95 8/18/95 8/19/95 8/20/95 8/21/95 AL 1 1 1 1 1 4 AR CT 3 DE 3 DC FL GA 1 3 2 2 5 1 IL 4 1 IN 1 1 KY 3 2 LA 1 2 1 ME 1 MD 3 3 MA 2 2 MI 1 1 M 1 NH 1 NJ 1 2 NY 2 NC 1 1 OH OK PA 1 1 RI 1 SC 1 TN 1 TX 1 1 6 6 1 VA 1 2 W WI TOT 0 0 0 5 3 8 1 7 8 5 7 12 9 6 19 2. General Representativeness of Episodic Ozone as Compared to Design Values In order to examine the representativeness of ozone levels during the episodes selected for modeling, a comparison was made between the daily maximum observed values to recent 2 An exceedance is a daily maximum 1-hour ozone concentration >=125 ppb. 5 ------- design values. In this analysis, the magnitude of county-specific design values for 1996-1998 were compared to the highest through 5th highest concentrations measured in the county during the three episodes. Counties with design values (DV) >120 ppb were selected for analysis in order to focus on concentrations approaching and exceeding the NAAQS. As can be seen in Table II-4, 64 percent of the 110 counties examined have design values within 15 ppb of the highest observed ozone in the Tier 2 episodes. Additionally, the second-high observed value yields more values below the design value than above it. The results indicate that the selected episodes contain measured ozone concentrations that are generally representative of recent design values over a large portion of the eastern U.S. Table II-4. Summary of Comparing the Five Highest Daily Maxima to Recent Design Values. Ranking of Observation within Tier 2 Days Highest ozone 2nd high ozone 3rd high ozone 4th high ozone 5th high ozone # of cases in which the observed was greater than the design value by 15 ppb 32 10 2 0 0 # of cases in which the observed was within 15 ppb of the design value 70 80 71 57 45 # of cases in which the observed was less than the design value by 15 ppb 8 20 37 53 65 B. Domain and Grid Configuration As with episode selection, there are also several considerations involved in selecting the domain and grid configuration to be used in the ozone modeling analysis. The modeling domain should encompass the area of intended analysis with an additional buffer of grid cells to minimize the effects of uncertain boundary condition inputs. Grid resolution should be equivalent to the resolution of the primary model inputs (emissions, winds, etc.) and equivalent to the scale of the air quality issue being addressed. For the eastern U.S., the regional/national Tier 2 analyses used the previously established OTAG domain to model regional ozone. The western U.S. domain is discussed in Section HI. The Tier 2 UAM-V modeling was completed using two grids of varying extent (shown in Figure II-1) and resolution as described below. Main Grid: Resolution: 1/2° longitude, 1/3° latitude (approximately 36 km) East-West extent: -99 W to -67 W North-South extent: 26 N to 47 N Vertical extent: Surface to 4 km Dimensions: 64 by 63 by 9 ------- Nested Grid3: Resolution: 1/6° longitude, 1/9° latitude (approximately 12 km) East-West extent: -92 W to -69.5 W North-South extent: 32 N to 44 N Vertical extent: Surface to 4 km Dimensions: 137 by 110 by 9 The vertical layers were consistent between the two grids: 0-50, 50-100, 100-300, 300- 600, 600-1000, 1000-1500, 1500-2000, 2000-2500, 2500-4000. All model heights are in meters above ground level. The number of vertical layers is greater than past regional-scale modeling applications (e.g., OTAG) and was intended to better capture the depth of the planetary boundary layer. This modeling domain allows for the consideration of future residual ozone exceedances and the effects of Tier 2 emissions reductions over most major metropolitan areas in the eastern U.S. (The Dallas-Fort Worth area may be the exception given its proximity to the western boundary.) Figure II-l. Map of the Tier 2 Eastern modeling domain. The outer box denotes the entire modeling domain (36 km) and the inner box indicates the fine grid location (12 km). 3 Model concentrations are not calculated for the outer periphery of the nested grid. Two buffer rows and columns are needed to solve the advection portion of the mass balance equation. ------- C. Meteorological Modeling In order to solve for the change in pollutant concentrations over time and space, the air quality model requires certain meteorological inputs that, in part, govern the formation, transport, and destruction of pollutant material. In particular, the UAM-V model used in the Tier 2 analyses requires five meteorological input files: wind (u- and v-vector wind components), temperature, water vapor mixing ratio, atmospheric air pressure, and vertical diffusion coefficient. Fine grid values of wind and vertical diffusivity are used; the other fine grid meteorological inputs are interpolated from the coarse grid files. The gridded meteorological data for the three historical 1995 episodes were developed by the New York Department of Environment and Conservation (NYDEC) using the Regional Atmospheric Modeling System (RAMS), version 3b. RAMS (Pielke et. a/., 1992) is a numerical meteorological model that solves the full set of physical and thermodynamic equations which govern atmospheric motions. The output data from RAMS, which is run in a polar stereographic projection and a sigma-p coordinate system, are then mapped to the UAM-V grid. Two separate meteorological UAM-V inputs, cloud fractions and rainfall rates, were developed based on observed data. RAMS was run in a nested-grid mode with three levels of resolution: 108 km, 36 km, and 12 km with 2S-344 vertical layers. The top of the surface layer was 16.7 m in the 36 and 12km grids. The two finer grids were at least as large as their UAM-V counterparts. In order to keep the model results in line with reality, the simulated fields were nudged to an European Center for Medium-Range Weather Forecasting (ECMWF) analysis field every six hours. This assimilation data set was bolstered by every four-hourly special soundings regularly collected as part of the North American Research Strategy on Tropospheric Ozone (NARSTO) field study in the northeast U.S. A summary of the settings and assorted input files employed in this RAMS application are listed below in Table D-5. For more detail on the meteorological model configuration, see Lagouvardos et al. (1997). A limited model performance evaluation (Sistla, 1999) was completed for a portion of the 1995 meteorological modeling (July 12-15). Observed data not used in the assimilation procedure were compared against modeled data at the surface and aloft. In general, there were no widespread biases in temperatures and winds. Furthermore, the meteorological fields were compared before and after being processed into UAM-V inputs. It was concluded that this preprocessing did not distort the meteorological fields. 4 34 layers were used in the inner nested grids. 28 layers were modeled in the outer 108 km grid. ------- Table II-5. Summary of RAMS model settings and inputs. Model Setting/Input File Input- Topography Input - Sea-surface temperature Input - Vegetation type Input - Initial conditions Input - Soil moisture Setting Setting - Lateral boundary conditions Setting - Horizontal diffusivity Setting - Vertical diffusivity Setting - Shortwave/Longwave radiation Description 30 arc -second data from EROS Data Center. Mean monthly climatological data from NCAR. 10 arc-minute data from NOAA/NGDC. The model was initialized with gridded one-degree ECMWF data sets prepared by the isentropic analysis package. Surface observations provided by SUNY were blended into the initialization fields. Six layer soil model. Assumed deeper layers were more moist than near-surface layers. Non-hydrostatic Klemp-Wilhelmson Smagorinsky Mellor and Yamada parameterization scheme Mahrer and Pielke D. Development of Other UAM-V Input Files The manmade emissions inventories for the five modeling scenarios were processed through EMS-95 (Alpine Geophysics, 1994) in order to develop the UAM-V-ready, day-specific emissions. Biogenic emissions were developed using the BEIS-2 model (Birth and Geron, 1995). In addition, the photochemical grid model requires several other types of data. In general, most of these miscellaneous model files were taken from existing regional modeling applications. Clean conditions were used to initialize the model and as lateral and top boundary conditions as in OTAG (OTAG, 1997). The model requires information regarding land use type and surface albedo for all Layer 1 grid cells in the domain. Existing OTAG data were used for these non-day-specific files. Photolysis rates were developed using the JCALC portion of the UAM-V modeling system. Turbidity values were set equal to a constant thought to be representative of regional conditions. E. Model Performance Evaluation The goal of the base year modeling was to reproduce the atmospheric processes resulting in high ozone concentrations over the eastern United States during the three 1995 episodes ------- selected for modeling. Note that the base year of the emissions was 1996 while the episodes are in 1995. The effects on model performance of using 1996 base year emissions for the 1995 episodes are expected to be small. An operational model performance evaluation for surface ozone for the 1995 episodes was performed in order to estimate the ability of the modeling system to replicate base year ozone concentrations. This evaluation is comprised principally of statistical assessments of model versus observed pairs. The robustness of an operational evaluation is directly proportional to the amount and quality of the ambient data available for comparison. 1. Statistical Definitions Below are the definitions of statistics used for the evaluation. The format of all the statistics is such that negative values indicate model ozone predictions that were less than their observed counterparts. Positive statistics indicate model overestimation of surface ozone. Statistics were not generated for the first three days of an episode to avoid the initialization period. The operational statistics were generated on a regional basis in accordance with the primary purpose of the modeling which is to assess the need for, and impacts of, a national mobile source emissions control program. The statistics were calculated for (a) the entire Tier 2 domain and (b) four quadrants (Midwest, Northeast, Southeast, Southwest). The statistics calculated for each of these areas are: Domainwide unpaired peak prediction accuracy: This metric simply compares the peak concentration modeled anywhere in the selected area against the peak ambient concentration anywhere in the same area. The difference of the peaks (model - observed) is then normalized by the peak observed concentration. Peak prediction accuracy: This metric averages the paired peak prediction accuracy calculated for each monitor in the subregion. It characterizes the capacity of the model to replicate peak (afternoon) ozone over a subregion. The daily peak model versus daily peak observed residuals are paired in space but not in time. Mean normalized bias: This performance statistic averages the normalized (by observation) difference (model - observed) over all pairs in which the observed values were greater than 60 ppb. A value of zero would indicate that the model over predictions and model under predictions exactly cancel each other out. Mean normalized gross error: The last metric used to assess the performance of the Tier 2/Sulfur base cases is similar to the above statistic, except in this case it is the absolute value of the residual which is normalized by the observation, and then averaged over all sites. A zero gross error value would indicate that all model concentrations (in which their observed counterpart was greater than 60 ppb) exactly matched the ambient values. 10 ------- 2. Evaluation Results As with previous regional photochemical modeling studies, the Tier 2 base year simulations are accurate representations of the historical ozone patterns at certain times and locations and poor representations at other times and locations over this large modeling domain. From a qualitative standpoint, there appears to be considerable similarity on most days between the observed and simulated ozone patterns. Additionally, where possible to discern, the model appears to follow the day-to-day variations in synoptic-scale ozone fairly closely. Other relevant observations, in terms of model performance, are listed below. • Mean normalized bias and mean normalized gross error values are similar to OTAG performance statistics for the entire domain and the four quadrants as summarized in Table II-6. Table II-6. Tier 2 Base Year model performance for the entire grid and by quadrant. Mean Normalized Bias Domain Midwest Northeast Southeast Southwest OTAG 1988 -8 -15 -3 +2 -6 OTAG 1991 -4 -8 -6 +15 +6 OTAG 1993 +1 -8 -8 +21 +2 OTAG 1995 +4 -5 +8 +9 +12 Tier 2 June 95 -10 -11 -17 -4 +2 Tier 2 July 95 -6 (-4)5 -13 (-8) -9 (-9) +4 (+5) +8 (+&} Tier 2 August 95 +2 +7 -9 +7 +6 Mean Normalized Gross Error Domain Midwest Northeast Southeast Southwest OTAG 1988 28 27 29 28 22 OTAG 1991 25 26 23 25 24 OTAG 1993 27 25 23 32 23 OTAG 1995 25 24 26 27 29 Tier 2 June 95 24 24 27 20 24 Tier 2 July 95 24 (24) 26 (25) 22 (21) 24 (24) 27 (26) Tier 2 August 95 23 22 24 22 24 5 Values in parentheses are for the 10-15th only. These dates correspond with OTAG 1995 episode days. 11 ------- In general, the model under predicts ozone for the June and July episodes (-10 and -6 percent, respectively). This underestimation bias generally occurs over the first half of an episode. The latter portions of these episodes are generally unbiased. Model performance is best over the southern portions of the domain. Mean normalized gross error ranges from 22 to 23 percent. Bias and errors are generally lowest in the Southwest region (fewest number of observed sites). The model typically underestimates the peaks as well as the mean ozone, but not as severely. Although the overall tendency (June/July episodes) is to underestimate the observed ozone, there are several instances in which large overestimations occurred. The model is slightly biased toward overestimation in the August episode (2.1 percent). Only the Northeast quadrant is underestimated (-9.1 percent) in this episode. While there are no established statistical criteria for evaluating the adequacy of regional modeling applications, the relatively low values of bias and error plus the OTAG- equivalent performance indicate the modeling is sufficient for a national assessment of the need for (and impact of) Tier 2/Sulfur controls. III. Ozone Modeling over the Western United States A. Episode Selection For the western modeling, there were no existing meteorological data sets suitable for regional ozone modeling. Measured ozone concentrations were examined for several recent years to find representative ozone episodes over the western U.S. As with the East, the ambient data were analyzed in order to identify time periods which captured episodic conditions in as many areas as possible. As a result of this analysis, two episodes from the summer of 1996 were selected for the western U.S. modeling: July 5-15 and July 18-31. An additional advantage associated with the selection of 1996 episodes is the meteorological inputs and emissions inputs are both from the same year. Nineteen episode days were modeled in all, not including the three ramp-up days used in both episodes to minimize the effects of initial conditions. 1. Episodic Meteorological Conditions and Ozone Levels July 5-15. 1996 July 6 marked the beginning of the development of a large 500 millibar ridge over the western U.S. A thermal low was located over the California desert and average summertime 12 ------- conditions (light winds, warm temperatures, and little to no precipitation) existed over much of the region. Ozone was high (i.e., greater than 125 ppb) in the Sacramento, San Joaquin Valley, and Los Angeles basins over the first five days of the episode. Most of the rest of the region did not experience elevated amounts of ozone, with the exception of the Salt Lake City region on July 7 and 8 (as high as 117 ppb). Over the period from July 11 to July 14, the ridge strengthened along the Pacific Coast displacing the jet stream north into Canada. Wind speeds aloft were quite low during this period along the West Coast resulting in little pollution advection or dispersion. Observed ozone values greater than 100 ppb were monitored in urban areas all along the Pacific Coast (Redding =110 ppb, Eugene =117 ppb, Portland = 145 ppb, Seattle/Tacoma =118 ppb). Elevated levels of ozone continued throughout this period in the Sacramento, the San Joaquin Valley, and Los Angeles airsheds. Further east, the highest ambient ozone of the summer in Albuquerque (111 ppb) and El Paso (112 ppb) were recorded on July 11. Vigorous northwest flow aloft which was associated with a trough over the central U.S. prevented ozone buildup in Denver (until July 15 when a 107 ppb value was recorded). Monsoonal rains kept ambient ozone relatively low in Arizona cities such as Phoenix and Tucson. The episode ended (even in Los Angeles and other California cities) when a strong 500 millibar trough progressed through the region from July 15-17. This period of reduced ozone conveniently allowed the modeling for the second July 1996 episode to be initialized with clean conditions (ozone and ozone precursors). July 18-31. 1996 Like the early July 1996 episode, this scenario began with a developing ridge over the western U.S. on July 21. Ozone was confined to the major cities of California until the July 23- 28 period, by which time the anticyclone aloft had strengthened and dominated the Pacific States and the Desert Southwest. Temperatures rose into the 100s in Oregon and Washington. No precipitation was recorded anywhere west of the Rocky Mountains; cloud cover was limited as well over this region. As a result, ozone levels rose as well in areas such as Portland (149 ppb on 7/26), Phoenix (127 ppb on 7/23), Seattle (112 ppb on 7/26) and Salt Lake City (110 ppb on 7/26). The ridge flattened out somewhat by July 29, but ozone values remained high in California and the Arizona cities (Phoenix) through the end of the episode. 2. General Representativeness of Episodic Ozone to Design Values Ozone in the western U.S. tends to be much less regionally pervasive than in the eastern U.S. In general, ozone non-attainment conditions are more local in nature. This makes it more 13 ------- difficult to capture in a few episodes all of the conditions that lead to individual design-value levels of ozone in western areas. Table ni-1 compares 1996-1998 ambient design values against the highest observed ozone levels in the Tier 2 episodes for the major non-California metropolitan areas in the West6. The results indicate that the peak ozone levels during the episodes modeled are generally in the range of the design values. For example, only in Portland, OR is the measured peak not within 15 ppb of the recent design value. Table III-l. Comparison of 1996-1998 design values for major metropolitan areas in the western U.S. against the highest observed ozone value in those same areas recorded during the periods July 8-15, 1996 and July 21-31, 1996. Area Albuquerque Denver El Paso Phoenix Portland Salt Lake City Seattle 1996- 1998 Design Value 97 112 123 120 133 123 121 Highest ozone recorded in the Tier 2 episode days 111 107 112 127 149 117 118 B. Domain and Grid Configuration The Tier 2 UAM-V modeling for the western U.S. was completed using a domain containing two nested grids of varying extent and resolution as described below and shown in Figure IJJ-1. The modeling used a latitude-longitude coordinate system as indicated below. Main Grid: Resolution: 1/2° longitude, 1/3° latitude (approximately 36 km) East-West extent: -127 W to -99 W North-South extent: 26 N to 52 N Vertical extent: Surface to 4800 meters Dimensions: 56 by 78 by 11 6 Capturing representative ozone levels in California is less important in this analysis because the equivalent of the Tier 2/Sulfur standards have been adopted there. 14 ------- Nested Grid7: Resolution: 1/6° longitude, 1/9° latitude (approximately 12 km) East-West extent: -125 W to -103 W North-South extent: 31 N to 49 N Vertical extent: Surface to 4800 meters Dimensions: 132 by 162 by 11 Figure III-l. Map of the Tier 2 modeling domain. The outer box denotes the entire modeling domain (36 km) and the inner box indicates the fine grid location (12 km). The vertical layers were consistent between the two grids: 0-50, 50-100, 100-300, 300-600, 600- 1000, 1000-1500, 1500-2000, 2000-2500, 2500-3100, 3100-3800, 3800-4800. All model heights are in meters above ground level. 7 Model concentrations are not calculated for the outer periphery of the nested grid. Two buffer rows and columns are needed to solve the advection portion of the mass balance equation. 15 ------- C. Meteorological Modeling The gridded meteorological data for the two historical 1996 episodes were developed using the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5). MM5 (Grell et. al., 1995) is a numerical meteorological model that solves the full set of physical and thermodynamic equations which govern atmospheric motions. MM5 was run in a nested-grid mode with three levels of resolution: 108 km, 36km, and 12 km with 23 vertical layers. The model was simulated in five day segments with an eight hour ramp-up period. The MM5 runs were started at OZ, which is 4PM PST. The first eight hours of each five day period were removed before being input into UAM-V. The UAM-V runs start at midnight, and each day runs from midnight to midnight (PST). MM5 is a terrain-following sigma-pressure coordinate model and was run using a Lambert conformal map projection, therefore the data were processed to match the UAM-V grid structure. There was also an issue in that several of the UAM-V grid boundaries extended slightly beyond their counterpart MM5 12 km and 36 km domain boundaries (mostly over the Pacific Ocean). In these cases, data from the next outer grid were mapped to these areas. A preprocessor (MM52UAMV) generates model-ready UAM-V files for wind, temperature, water vapor, pressure, and vertical diffusion from the MM5 output. For more information on the preparation of non-emissions-related inputs, see SAI (1999). The standard version of MM5 was revised for this project to output the internally- calculated vertical diffusivities (Kv) generated as part of the Medium Range Forecast (MRF) model boundary layer scheme. When the MRF boundary layer option is employed these Kv values represent non-local vertical exchanges. This approach should provide the most representative mixing field; one that captures both large- and small-scale vertical diffusive fluxes. Unlike the eastern UAM-V modeling, the cloud fraction and rainfall rate inputs were derived from the meteorological model as opposed to interpolating observed data to the model grid. This alternative procedure was used because of the relatively sparse meteorological observation network in the western U.S. Cloud fractions were diagnosed from the MM5 results based on the assignment of a critical relative humidity, which if exceeded, indicated the presence of a cloud. The fractional extent of the cloud was a function of the amount the model humidity exceeds the threshold value. Rainfall rates are extracted directly from MM5. D. Development of Other UAM-V Input Files The manmade emissions for the three scenarios were processed through the Sparse Matrix Operator Kernal Emissions (SMOKE) modeling system (Houyoux and Vukovich, 1999) for stationary sources and EMS-95 for mobile sources in order to develop the model-ready, day- 16 ------- specific emissions. Biogenic emissions were developed using BEIS-2. The initial, lateral boundary, and top boundary species concentrations were set to clean values intended to represent background-like concentrations. All species were set to values prescribed in EPA (1991), except CO (200 ppb), VOC (25 ppb)8, ozone (30 ppb), NO (0 ppb), and NO2 (1 ppb). Model land use characteristics (as percentages of specified 11 categories) were derived from a 200 meter resolution U.S. Geological Survey data base. Albedo values (needed in the calculation of photolysis rates) were taken from this same data base. The aerosol optical depth which governs the amount of UV scattering due to airborne particulates was set to a nominal value (0.094) indicative of rural conditions. The photolysis rate lookup table required for the UAM-V runs was developed, as in the eastern U.S. simulations, via the JCALC preprocessor program. E. Model Performance Evaluation An operational evaluation was performed for the western modeling using the same procedures and statistics discussed in section II-E. Model performance measures were calculated over the entire modeling domain, the 12 km fine grid, and 10 individual areas (Albuquerque, Denver, El Paso, Phoenix, Portland, Salt Lake City, the San Joaquin Valley, Seattle, San Francisco, and Southern California). Table ni-2 contains the operational evaluation statistics. Table III-2. Model performance statistics for individual areas in the western U.S. Region Albuquerque Denver El Paso Phoenix Portland Salt Lake City San Joaquin Valley Seattle San Francisco Southern California Unpaired Peak Prediction Accuracy -0.287 -0.190 -0.356 -0.292 -0.041 -0.225 -0.309 0.017 -0.404 -0.436 Average Peak Prediction Accuracy -0.313 -0.287 -0.400 -0.326 -0.180 -0.308 -0.390 -0.200 -0.375 -0.505 Mean Normalized Bias -0.331 -0.318 -0.443 -0.361 -0.227 -0.343 -0.404 -0.211 -0.395 -0.524 Mean Normalized Gross Error 0.331 0.319 0.443 0.362 0.263 0.348 0.406 0.284 0.396 0.530 Model performance for the 1996 base year episodes was characterized by underestimates Divided across CB-FV VOC species as specified in EPA guidance. 17 ------- of observed ozone as summarized in Table in-3. The model underestimated mean ozone by about 30 ppb on average. On an individual area basis, the model did somewhat better in the northwest portion of the domain (Portland and Seattle) where negative biases were about 15-25 percent. In the end, the modeling was determined to be sufficient for the intended purpose, that is, to be used in a relative sense to assess the impacts of the Tier 2/Sulfur controls as part of the economic benefits calculations. Table III-3. Summary model performance statistics for surface ozone in the western U.S. fine grid. (Units in percent) Peak prediction accuracy Mean normalized bias Mean normalized gross error Episode 1 (July 5 -15, 1996) -39.0 -40.6 40.8 Episode 2 (July 2 1-31, 1996) -37.5 -38.6 39.1 IV. Results of the Tier 2/Gasoline Sulfur Modeling for Ozone The results of the Tier-2 modeling were further analyzed to provide information to (a) support the determination of the need for Tier 2, (b) examine the air quality impacts of the program, and (c) support the preparation of responses to comments on the proposed rulemaking. The analyses for each of these purposes are described below. A. Analysis of Need To support the determination of the need for Tier 2, the modeling results were examined to identify those CMSA and MSAs that have predicted exceedances of the 1-hour NAAQS in the 2007 and/or in the 2030 baseline scenarios. Model predicted exceedances are defined as daily 1- hour maximum concentrations >=125 ppb. A CMSA/MSA is determined to contain an exceedance if at least one of the model grid cells assigned to the area has at least one exceedance during the episodes modeled. The procedures for assigning grid cells to areas are defined below. The CMS A/MS As with predicted 2007 base case and/or 2030 base case exceedances are listed in Appendix IV-1. B. Impacts of the Tier 2/Sulfur Program on Ozone Levels The forecasted impacts on ozone concentrations as a result of the Tier 2 program were analyzed by comparing model predictions from the 2007 and 2030 Tier 2 control cases to those in the corresponding 2007 and 2030 base case scenarios. For 2007, the impacts of Tier 2 are derived from model simulations for the three episodes in June, July, and August 1995. For 2030, 18 ------- model runs were made for the June and July 1995 episodes only. Thus, the 2030 impacts are based on predictions from these two episodes. As indicated above, the first 3 days of each episode are considered as initialization or "ramp up" days and are, therefore, excluded from the analysis of results. The focus of the analysis is on ozone levels in the eastern U.S. since this region contains most of the areas with 1-hour nonattainment. In particular, the impacts were quantified for CMSA/MSAs with predicted future-year exceedances of the 1-hour ozone NAAQS. As indicated above, predicted exceedances are modeled 1-hour daily maximum concentrations >=125 ppb. The methods for determining which areas have forecasted exceedances are described below. For the eastern U.S. modeling there were 48 such areas in 2007 while for 2030 there were 38 areas with base case exceedances9. Note that the 2030 scenario had a different number of areas for analysis compared to 2007 in part because some areas only had exceedances predicted in the August episode which was not modeled for 2030 (i.e., Charleston, Cincinnati, Huntington, Indianapolis, Lakeland, Macon, Melbourne, Norfolk, Orlando, Pensacola, and Wheeling) and because one area (i.e., York) had predicted exceedances in the 2030 base case but not the 2007 base case. 1. Methods for Quantifying Impacts a. Definition of Areas for Analysis In order to analyze the impacts of the Tier 2/Sulfur emissions reductions, it was necessary to "link" or assign the model's grid cells to individual CMSA/MSAs. The rules for assigning grid cells to CMSA/MSAs (i.e., areas) is as follows. The first step was to assign grid cells to States based on the fraction of the grid cells' area in a State. A grid cell was assigned to the State which contains most of the cells' area. Next, grid cells were assigned to an individual CMSA/MSAs if (1) the grid is wholly contained within the CMSA/MSA or (2) partially within (i.e., overlapping) the area, but not also partially within another CMSA/MSA. Grid cells that partially overlap two or more CMSA/MSAs are assigned to the county, and thereby the corresponding CMSA/MSA, which contains the largest portion of the grid cell. Each grid cell in the "coarse" or 36 km grid portion of the domain was divided into nine 12 km grids before applying the preceding methodology. The number of grid cells assigned to each area is listed in Appendix IV-2. b. Description of Ozone Metrics The impacts of Tier 2 on ozone were quantified using a number of metrics (i.e., measures of ozone concentrations). These metrics include: 9 These areas are listed in Appendix IV-1. Portland, OR is on the list of areas with predicted future exceedances, but was not included in this analysis which focused only on areas in the eastern U.S. 19 ------- (1) the peak 1-hour ozone concentrations, (2) the number of exceedances, (3) the total amount of ozone >= 125 ppb, (4) the decrease in ozone, on average, and (5) the increase in ozone, on average. (1) The peak 1-hour ozone represents the highest ozone prediction within the area (i.e., CMSA or MSA) across all episodes modeled. (2) The number of exceedances is the total number of grid cells with predicted exceedances in the area across all days. This exceedance metric counts each grid cell every day there is a predicted exceedance in that grid. Thus, an individual grid cell can be counted more than once if there are multiple days with predicted exceedances in that grid. (3) The total amount of ozone above 125 ppb in an area is determined by taking the difference between the predicted daily maximum ozone concentration and 125 ppb (i.e., daily maximum - 125 ppb) in each grid cell and then summing this amount across all grid cells in the area and days modeled. This metric is referred to as "the amount of nonattainment". (4) The decrease, on average is determined by first summing all the reductions predicted in those grid cells with daily maximum ozone >=125 ppb in the base case (i.e., base case exceedances). This total reduction is then divided by the number of base case exceedances in the area to yield the "ppb" decrease that occurs, on average, for the exceedances predicted in the area. (5) The increase, on average is determined by summing any increases in ozone that occur in values already >= 125 ppb in the base case together with any increases that cause a value below 125 ppb in the base case to go above 125 ppb in the control case. This total increase is then divided by the number of exceedances in the base case. The impacts of Tier 2 on ozone were examined for the individual CMSA/MSAs as well as by aggregating the metrics across all areas (i.e., 48 in 2007 and 38 in 2030) to obtain the overall impact expected from the program. The values of the metrics are provided in Appendix IV-3 for 2007 and Appendix IV-4 for 2030. 2. Impacts on Ozone in 2007 and 2030 a. Impacts in 2007 The ozone modeling results for 2007 show that Tier 2 will provide nearly a 10% reduction in the total number of exceedances predicted across all 48 CMSA/MSAs combined. Overall, the total amount of nonattainment is predicted to decline by about 15%. Looking at the results for individual areas, over half (i.e., 31 of the 48) areas have fewer exceedances with Tier 2 in 2007. In five of these 31 areas, the exceedances are eliminated by Tier 2 (i.e., Macon, 20 ------- Melbourne, Norfolk, Pittsburgh, and Rochester). Of the 48 areas, 14 are expected to have no change in the total number of exceedances. Only 3 areas (i.e., Chicago, Detroit, and New London) are predicted to have an increase in exceedances. In each of these areas the increase occurs in only one grid cell on one day, which is a small impact considering the total number of grid cells in these area. In the vast majority of areas (i.e., 45 out of 48), the decrease in ozone, on average is greater than any predicted increase, on average. In fact, only two areas (i.e., Chicago and Detroit) are predicted to have an increase, on average of more than a half ppb (6 other areas had an increase, on average of between one tenth and a half ppb). However, even these two areas are predicted to have a reduction in the peak ozone concentration. b. Impacts in 2030 In the 2030 base case the number of exceedances increases relative to the amount of 125 ppb or greater cells in the 2007 base case in most areas, when looking at a consistent set of episode days. This reflects the increase in emissions as growth outpaces the effects of controls. Meanwhile, the overall number of exceedances is reduced 32% due to the Tier 2/Sulfur controls in 2030 while the amount of nonattainment is predicted to decline by 36%. Of the 38 areas, nearly all (i.e., 32) are predicted to have a decrease in the number of exceedances while the remaining six areas are predicted to have no change. None of the areas are predicted to have an increase in exceedances. However, in two of the 38 areas (i.e., Chicago and Detroit) the amount of ozone nonattainment is predicted to be larger in the control case than in the 2030 base case. C. Additional Analyses to Support Responses to Comments Several additional analyses were performed to support the responses to comments on the proposed rule. These analyses include (a) an evaluation of the Tier 2 regional model performance analogous to EPA's urban ("local") scale model performance recommendations (EPA, 1991), (b) a determination of "alternative attainment targets" for individual areas considering the episodes modeled, and (c) an estimation of attainment/nonattainment based on relative reduction factors. 1. "Local" Scale Model Performance Several comments were received on the Tier 2 notice of proposed rulemaking to the effect that model over predictions could be leading to overestimates of residual non-attainment areas. To support the response to this comment, a local-scale evaluation was conducted on the final modeling to ensure that the determination of Tier 2/Sulfur need was not significantly biased 21 ------- due to model performance. Statistics were calculated for 36 "local" subregions10 using the procedures described in Section II. Generally speaking, model performance will typically appear poorer for individual local subregions than when the performance statistics are averaged over larger regions. This results due to the heightened sensitivity of local-scale model results to local input uncertainties. Table IV-1 contains the area-wide unpaired peak prediction accuracy, the average peak prediction accuracy, mean normalized bias and mean normalized gross error values for the 36 local-scale subdomains averaged over the 30 episode days. Some conclusions regarding model performance at the local scale are listed below. Table IV-1. Tier 2 Base Case model performance for 36 local subregions. Region Dallas Houston-Galveston Beaumont-Port Arthur Baton Rouge New Orleans St. Louis Memphis Birmingham Atlanta Nashville Knoxville Charlotte Greensboro Raleigh-Durham Evansville-Owensboro Indianapolis Louisville Cincinnati-Dayton Columbus Huntington-Ashland Chicago Milwaukee Muskegon-Grand Rapids Gary-South Bend Detroit Pittsburgh Central PA Norfolk Richmond Baltimore-Washington Delaware Philadelphia Unpaired Peak Prediction Accuracy -0.140 -0.098 -0.023 0.070 0.285 -0.008 0.133 0.140 0.151 0.163 -0.031 0.153 0.099 0.039 0.170 0.011 0.143 0.005 0.005 0.167 0.217 0.333 0.124 0.022 0.095 0.010 0.094 0.149 0.203 0.022 0.080 -0.013 Average Peak Prediction Accuracy -0.102 0.059 0.103 0.223 0.143 -0.037 -0.128 0.073 0.014 0.050 -0.217 0.010 -0.015 -0.090 0.004 -0.113 0.068 -0.053 -0.109 0.123 -0.227 -0.187 -0.142 -0.236 -0.190 -0.101 -0.083 -0.078 0.007 -0.067 -0.071 -0.166 Mean Normalized Bias 0.192 0.248 0.202 0.289 0.222 0.193 0.215 0.171 0.225 0.262 0.271 0.173 0.174 0.176 0.230 0.206 0.271 0.225 0.196 0.235 0.288 0.234 0.221 0.286 0.270 0.228 0.218 0.221 0.183 0.199 0.150 0.251 Mean Normalized Gross Error -0.085 0.006 0.052 0.178 0.141 -0.029 -0.108 0.034 -0.008 0.096 -0.188 0.001 -0.007 -0.098 0.037 -0.056 0.096 -0.034 -0.095 0.070 -0.058 -0.026 -0.090 -0.128 -0.115 -0.056 -0.048 -0.064 -0.013 -0.068 -0.072 -0.135 10 For evaluation purposes, these local areas were defined by simple boxes of grid cells around a given area. They do not correspond to non-attainment areas or CMS A/MS As. 22 ------- New York City Hartford Boston Maine 0.130 0.054 0.186 0.189 -0.190 -0.125 -0.147 -0.147 0.281 0.220 0.238 0.224 -0.120 -0.102 -0.076 -0.042 The model is, on average, biased toward underestimations of observed ozone, especially in the Midwest and Northeast. The few regions which exhibit overestimated base year ozone are in the southern portion of the domain. Baton Rouge, New Orleans, Birmingham, and Houston all have positive biases of at least 5 percent. Model performance is generally poorest in the Lake Michigan area probably due to the preponderance of shoreline monitors (where the highest ozone levels are often confined to within 1-3 km inland from the cool lake, that is, below the resolution of this analysis). Gross errors in this region approach 30 percent. 2. Determination of Alternative Attainment Targets As indicated above, one of the primary purposes of the Tier 2 modeling was to determine the number of areas projected to experience exceedances of the 1-hour standard in 2007. These areas were identified based on whether the highest daily maximum 1-hour value in the 2007 base case was >=125 ppb. This "exceedance method" was criticized during the comment period as potentially exaggerating the extent of the future year problem. One commenter recommended that EPA follow its own guidance (EPA, 1996) regarding attainment demonstrations for ozone episode days associated with infrequent, severe meteorological conditions by allowing for alternative attainment targets (i.e., values above 124 ppb). These alternative targets are allowed for use in local attainment demonstrations to determine whether model-predicted peak ozone values > 124 ppb can be considered as showing attainment in view of the statistical form of the 1-hour NAAQS in the case of especially severe meteorological conditions. To respond to this and similar comments we performed an analysis to identify the alternative attainment targets appropriate for the episodes modeled. This analysis was performed for those 18 areas for which the meteorological severity has been calculated and ranked. Alternative targets were not be calculated for the other areas. The projected exceedance areas for which this analysis was performed are: Atlanta, Baltimore, Baton Rouge, Birmingham, Chicago, Greater Connecticut, Cleveland, Detroit, Houston, Huntington, Louisville, Milwaukee, Muskegon, New York, Philadelphia, Pittsburgh, Providence, and St. Louis. Table IV-2 shows the alternative targets for those days/areas thought to be representative of unusually severe meteorology. Some episode days are not shown because there are no alternative targets for those days (i.e., the target remains 124 ppb for all areas). Each residual 23 ------- nonattainment area (for which data was available to complete the alternate target analysis) had at least one day in which the 2007 baseline maxima exceeded the attainment target, 124 ppb or otherwise. Table IV-2. Alternative attainment targets by non-attainment area and episode day. Dallas Houston Baton Rouse St. Louis W. Lake Michigan Lake Michigan Birmingham E. Lake Michigan Louisville Atlanta Cincinnati Pittsburgh Washington B.C. Baltimore Philadelnhia New York Citv Greater CT Boston Providence 6/16 130 6/18 130 130 6/19 130 130 130 6/20 130 130 6/21 130 6/22 130 6/24 139 7/11 137 130 7/12 150 130 130 130 130 7/13 132 137 130 130 7/14 130 146 130 132 130 130 7/15 135 130 144 140 151 134 130 8/12 130 8/15 153 130 8/16 131 130 8/17 130 130 8/18 130 137 8/19 130 8/20 130 3. Estimation of Attainment/Nonattainment Using Relative Reduction Factors EPA received comments that recommended using relative reduction factors applied to ambient design values as an approach to estimate future nonattainment. Specifically, the commenters recommended that EPA follow draft guidance for demonstrating attainment of the 8- hour NAAQS for such an analysis (EPA, 1999). In response, we calculated relative reduction factors for the 2007 base case and control scenarios using the general methodology in this guidance. The exceptions to guidance are that: (a) relative reduction factors (RRF) were calculated for the highest design value in a county rather than for all monitoring sites in a county and (b) we used a cut-off of 80 ppb as appropriate for considering 1-hour model predictions as opposed to 70 ppb recommended in the guidance for 8-hour concentrations (see Appendix IV-5 for the rationale for selecting 80 ppb). The county-specific relative reduction factors (2007-B RRF, 2007-C RRF) were applied to both 1995-1997 and 1996-1998 design values for estimating 2007 base and 2007 control values (2007-B New DV, 2007-C New DV). The ambient design values, adjustment factors based on modeling, and the resulting 2007 values are provided in 24 ------- Appendix IV-6. The areas which have estimated 2007 base case design values >=125 ppb in using either 1995-1997 and/or 1996-1998 design values are listed in Table IV-3. Table IV-3. Areas which have estimated 2007 base case design values >=125 ppb. Chicago, IL CMSA Dallas, TX CMSA Houston, TX CMSA New York City, NY CMSA Philadelphia, PA CMSA Washington, DC-Baltimore, MD CMSA Atlanta, GA CMSA Baton Rouge, LA MSA Beaumont, TX MSA Grand Rapids, MI MSA Hartford, CT MSA New London, CT MSA Houma, LA MSA Longview, TX MSA Sheboygan, WI MSA Iberville Parish, LA La Porte County, IN Manitowoc County, WI 25 ------- V. References Alpine Geophysics, 1994: Technical Formulation Document: SARMAP/LMOS Emissions Modeling System (EMS-95), Pittsburgh, PA. Birth, T.L. and C.D. Geron, 1995: User's Guide to the Personal Computer Version of the Biogenic Emissions Inventory System (PC-BEIS), Version 2.O., U.S. Environmental Protection Agency, Research Triangle Park, NC. Douglas, S.G., Hudischewskyj, A.B., and A.R. Alvarez, 1999: Preparation of Non-Emissions- Related Inputs for Application of the UAM-V Modeling System to the Western U.S., ICF Consulting Inc., Systems Applications International Inc., San Rafael CA. E.H. Pechan and Associates, 1999: Procedures for Developing Base Year and Future Year Mass and Modeling Inventories for the Tier 2 Final Rulemaking, Report to U.S. Environmental Protection Agency, Research Triangle Park, NC. EPA, 1991: Guideline For Regulatory Application of the Urban Airshed Model, Office of Air Quality Planning and Standards, Technical Support Division, Source Receptor Analysis Branch, Research Triangle Park, NC. EPA, 1996: Guidance on Use of Modeled Results to Demonstrate Attainment, Office of Air Quality Planning and Standards, EPA-454/B-95-007, Research Triangle Park, NC. EPA, 1999: Draft Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-Hour Ozone NAAQS, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Grell, G.A., Dudhia, I, and D.R. Stauffer, 1995: A Description of the Fifth-Generation Penn State/NCARMesoscale Model (MM5), NCAR Technical Note, NCAR/TN-398, Boulder CO. Houyoux, M., R. and J. M. Vukovich, 1999: Updates to the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System and Integration with Models-3. Presented at The Emission Inventory: Regional Strategies for the Future, 26-28 October 1999, Raleigh, NC, Air & Waste Management Association. Lagouvardos, K., Kallos, G., and V. Kotroni, 1997: Modeling and Analysis of Ozone and its Precursors in the Northeast U.S.A. (Atmospheric Model Simulations), University of Athens, Department of Physics, Laboratory of Meteorology, Athens. OTAG, 1997. "OTAG Technical Support Document, Chapter 2: Regional Scale Modeling Workgroup," Des Plaines, IL. 26 ------- Pielke, R.A., W.R. Cotton, R.L. Walko, CJ. Tremback, W.A. Lyons, L.D. Grasso, M.E. Nicholls, M.D. Moran, D.A. Wesley, TJ. Lee, and J.H. Copeland, 1992: A Comprehensive Meteorological Modeling System - RAMS, Meteor. Atmos. Phys., 49. 69-91. Sistla, Gopal, 1999: Personal communication. Systems Applications International, 1996: User's Guide to the Variable-Grid Urban Airshed Model (UAM-V), SYSAPP-96-95/27r, San Rafael CA. 27 ------- Appendix IV-1: Areas with Predicted Exceedances in 2007 and/or 2030 Base Case Scenarios CMSA/MSAs Boston, MA CMSA Chicago, IL CMSA Cincinnati, OH CMSA Cleveland, OH CMSA Detroit, MI CMSA Houston, TX CMSA Milwaukee, WI CMSA New York City, NY CMSA Philadelphia, PA CMSA Washington, DC-Baltimore, MD CMSA Atlanta, GA MSA Barnstable, MA MSA Baton Rouge, LA MSA Benton Harbor, MI MSA Biloxi,MS MSA Birmingham, AL MSA Buffalo, NY MSA Canton, OH MSA Charleston, WV MSA Charlotte, NC MSA Grand Rapids, MI MSA Hartford, CT MSA Houma, LA MSA Huntington, WV MSA Indianapolis, IN MSA Jackson, MS MSA 2007 Base Case Exceedance: June/July/August Episodes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 2030 Base Case Exceedance: June/July Episodes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes No No Yes IV-1-1 ------- Lafayette, LA MSA Lakeland, FL MSA Louisville, KY MSA Macon, GA MSA Melbourne, FL MSA Memphis, TN MSA Nashville, TN MSA New London, CT MSA New Orleans, LA MSA Norfolk, VA MSA Orlando, FL MSA Pensacola, FL MSA Pittsburgh, PA MSA Portland, OR MSA Providence, RI MSA Richmond, VA MSA Rochester, NY MSA Rockford, IL MSA St. Louis, MO MSA Sarasota, FL MSA Tampa, FL MSA Toledo, OH MSA Wheeling, WV MSA York, PA MSA Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Not Applicable Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes No Yes No No Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes IV-1-2 ------- Appendix IV-2: Number of 12km Grid Cells Assigned to Each CMSA/MSA CMSA/MSAs Boston, MA CMSA Chicago, IL CMSA Cincinnati, OH CMSA Cleveland, OH CMSA Detroit, MI CMSA Houston, TX CMSA Milwaukee, WI CMSA New York City, NY CMSA Philadelphia, PA CMSA Washington, DC-Baltimore, MD CMSA Atlanta, GA MSA Barnstable, MA MSA Baton Rouge, LA MSA Benton Harbor, MI MSA Biloxi, MS MSA Birmingham, AL MSA Buffalo, NY MSA Canton, OH MSA Charleston, WV MSA Charlotte, NC MSA Grand Rapids, MI MSA Hartford, CT MSA Houma, LA MSA Huntington, WV Indianapolis, IN MSA Jackson, MS MSA Total Number of Grid Cells in Area 189 129 71 68 126 132 39 195 118 187 115 19 30 15 41 64 35 27 31 69 58 41 51 47 71 48 IV-2-1 ------- Lafayette, LA MSA Lakeland, FL MSA Louisville, KY MSA Macon, GA MSA Melbourne, FL MSA Memphis, TN MSA Nashville, TN MSA New London, CT MSA New Orleans, LA MSA Norfolk, VA MSA Orlando, FL MSA Pensacola, FL MSA Pittsburgh, PA MSA Providence, RI MSA Richmond, VA MSA Rochester, NY MSA Rockford, IL MSA St. Louis, MO MSA Sarasota, FL MSA Tampa, FL MSA Wheeling, WV MSA York, PA MSA 56 21 45 37 32 58 78 12 96 60 61 34 80 20 66 71 32 127 23 56 21 20 IV-2-2 ------- Appendix IV-3: Ozone Metrics for 2007 Base Case and Tier 2/Sulfur Control Case1 June/July/August Episodes Peak (ppb) 2007 Base 2007 Control # Exceedance 2007 Base 2007 Control % Change Total Nonattainment (ppb) 2007 Base 2007 Control % Change Total ppb Decrease Decrease, on Average Total ppb Increase Increase, on Average 2007 Base Case vs 2007 Tier 2/Sulfur Control Case CMSA/MSA Composite N/A N/A 1170 1056 -9.7% 12167 10384 -14.6% 1986 1.7 76 0.1 Atlanta 178 173 109 87 -20.2% 1713 1227 -28.4% 535 4.9 0 0.0 Barnstable 147 145 5 4 -20.0% 47 39 -17.0% 9 1.8 0 0.0 Baton Rouge 152 151 95 86 -9.5% 800 692 -13.5% 116 1.2 0 0.0 Benton Harbor 151 150 9 9 0.0% 93 89 -4.3% 5 0.6 0 0.0 Biloxi 142 141 30 24 -20.0% 123 94 -23.6% 32 1.1 0 0.0 Birmingham 138 133 12 7 -41.7% 68 37 -45.6% 41 3.4 1 0.1 Boston 145 143 12 9 -25.0% 88 65 -26.1% 24 2.0 0 0.0 Buffalo 133 133 o J 3 0.0% 15 13 -13.3% 2 0.7 0 0.0 Canton 130 129 2 2 0.0% 7 4 -42.9% 3 1.5 0 0.0 1 Note that values for total non-attainment, total ppb decrease, and total ppb increase are all rounded to the nearest "ppb". IV-3-1 ------- June/July/August Episodes Peak (ppb) 2007 Base 2007 Control # Exceedances 2007 Base 2007 Control % Change Total Nonattainment (ppb) 2007 Base 2007 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) 2007 Base Case vs 2007 Tier 2/Sulfur Control Case CMSA/MSA Charleston 139 138 2 2 0.0% 18 15 -16.7% 2 1.0 0 0.0 Charlotte 142 139 2 2 0.0% 25 19 -24.0% 6 3.0 0 0.0 Chicago 152 150 13 14 7.7% 119 123 3.4% 5 0.4 11 0.8 Cincinnati 138 137 8 7 -12.5% 44 42 -4.5% 6 0.8 3 0.4 Cleveland 136 138 4 3 -25.0% 20 19 -5.0% 4 1.0 2 0.5 Detroit 148 147 11 12 9.1% 106 124 17.0% 4 0.4 23 2.1 Grand Rapids 154 154 43 42 -2.3% 657 619 -5.8% 40 0.9 2 0.0 Hartford 172 170 17 17 0.0% 345 327 -5.2% 18 1.1 0 0.0 Houma 146 145 68 59 -13.2% 409 356 -13.0% 58 0.9 0 0.0 Houston 153 153 62 58 -6.5% 471 418 -11.3% 56 0.9 2 0.0 IV-3-2 ------- June/July/August Episodes Peak (ppb) 2007 Base 2007 Control # Exceedances 2007 Base 2007 Control % Change Total Nonattainment (ppb) 2007 Base 2007 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) 2007 Base Case vs 2007 Tier 2/Sulfur Control Case CMSA/MSA Huntington 148 146 6 6 0.0% 65 54 -16.9% 10 1.7 0 0.0 Indianapolis 128 126 2 2 0.0% 4 2 -50.0% 3 1.5 0 0.0 Jackson 130 128 4 3 -25.0% 12 5 -58.3% 7 1.8 0 0.0 Lafayette 142 141 34 32 -5.9% 236 205 -13.1% 32 0.9 0 0.0 Lakeland 130 128 2 1 -50.0% 7 3 -57.1% 5 2.5 0 0.0 Louisville 150 149 21 21 0.0% 235 224 -4.7% 17 0.8 6 0.3 Macon 127 124 1 0 -100.0% 2 0 -100.0% 4 4.0 0 0.0 Melbourne 128 124 1 0 -100.0% 3 0 -100.0% 3 3.0 0 0.0 Memphis 150 148 6 5 -16.7% 78 68 -12.8% 12 2.0 0 0.0 Milwaukee 131 129 4 4 0.0% 10 10 0.0% 2 0.5 2 0.5 IV-3-3 ------- June/July/August Episodes Peak (ppb) 2007 Base 2007 Control # Exceedances 2007 Base 2007 Control % Change Total Nonattainment (ppb) 2007 Base 2007 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) 2007 Base Case vs 2007 Tier 2/Sulfur Control Case CMSA/MSA Nashville 154 151 6 5 -16.7% 96 71 -26.0% 25 4.2 0 0.0 New London 159 157 18 19 5.6% 255 232 -9.0% 23 1.3 0 0.0 New Orleans 160 160 149 138 -7.4% 1252 1135 -9.3% 122 0.8 0 0.0 New York City 179 178 170 163 -4.1% 2064 1897 -8.1% 194 1.1 22 0.1 Norfolk 125 124 1 0 -100.0% 0 0 0.0% 1 1.0 0 0.0 Orlando 135 132 5 3 -40.0% 24 11 -54.2% 15 3.0 0 0.0 Pensacola 129 126 2 2 0.0% 8 3 -62.5% 5 2.5 0 0.0 Philadelphia 138 136 20 20 0.0% 110 79 -28.2% 32 1.6 0 0.0 Pittsburgh 127 124 2 0 -100.0% 4 0 -100.0% 5 2.5 0 0.0 IV-3-4 ------- June/July/August Episodes Peak (ppb) 2007 Base 2007 Control # Exceedances 2007 Base 2007 Control % Change Total Nonattainment (ppb) 2007 Base 2007 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) 2007 Base Case vs 2007 Tier 2/Sulfur Control Case CMSA/MSA Providence 149 147 13 11 -15.4% 148 128 -13.5% 22 1.7 0 0.0 Richmond 151 149 11 11 0.0% 193 169 -12.4% 24 2.2 0 0.0 Rochester 125 124 1 0 -100.0% 0 0 0.0% 0 0.0 0 0.0 Rockford 129 128 1 1 0.0% 4 o J -25.0% 1 1.0 0 0.0 Sarasota 160 155 23 17 -26.1% 249 188 -24.5% 70 3.0 0 0.0 St. Louis 145 141 8 7 -12.5% 66 45 -31.8% 23 2.9 0 0.0 Tampa 171 167 77 69 -10.4% 1174 947 -19.3% 237 3.1 1 0.0 Toledo 132 131 1 1 0.0% 7 6 -14.3% 0 0.0 0 0.0 Washington- Baltimore 155 154 72 67 -6.9% 690 576 -16.5% 123 1.7 1 0.0 Wheeling 128 126 2 1 -50.0% 3 1 -66.7% o 3 1.5 0 0.0 IV-3-5 ------- Appendix IV-4: Ozone Metrics for 2030 Base Case and Tier 2/Sulfur Control Case1 June/July Episodes Peak (ppb) 2030 Base 2030 Control # Exceedances 2030 Base 2030 Control % Change Total Nonattainment (ppb) 2030 Base 2030 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) # Exceedances 2007 Base 2007 Control % Change 2030 Base Case vs 2030 Tier 2/Sulfur Control Case CMSA/MSA Composite N/A N/A 708 479 -32.3% 8122 5165 -36.4% 4248 6.0 276 0.4 Atlanta 173 162 52 20 -61.5% 772 182 -76.4% 938 18.0 0 0.0 Barnstable 153 146 5 4 -20.0% 71 37 -47.9% 35 7.0 0 0.0 Baton Rouge 154 149 70 55 -21.4% 706 499 -29.3% 237 3.4 0 0.0 Benton Harbor 154 152 9 9 0.0% 110 91 -17.3% 19 2.1 0 0.0 Biloxi 138 136 23 12 -47.8% 91 38 -58.2% 71 3.1 0 0.0 Birmingham 139 131 7 2 -71.4% 45 11 -75.6% 55 7.9 1 0.1 Boston 148 138 15 5 -66.7% 115 28 -75.7% 129 8.6 0 0.0 Buffalo 134 131 3 2 -33.3% 17 10 -41.2% 7 2.3 0 0.0 Canton 134 127 3 1 -66.7% 16 2 -87.5% 19 6.3 0 0.0 2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance Composite 563 514 -8.7% Atlanta 42 33 -21.4% Barnstable 5 4 -20.0% Baton Rouge 57 52 -8.8% Benton Harbor 9 9 0.0% Biloxi 17 13 -23.5% Birmingham 6 3 -50.0% Boston 12 9 -25.0% Buffalo 3 3 0.0% Canton 2 2 0.0% 1 Note that values for total non-attainment, total ppb decrease, and total ppb increase are all rounded to the nearest "ppb". IV-4-1 ------- June/July Episodes Peak (ppb) 2030 Base 2030 Control # Exceedances 2030 Base 2030 Control % Change Total Nonattainment (ppb) 2030 Base 2030 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) # Exceedances 2007 Base 2007 Control % Change 2030 Base Case vs 2030 Tier 2/Sulfur Control Case CMSA/MSA Charlotte 148 140 2 1 -50.0% 35 15 -57.1% 22 11.0 0 0.0 Chicago 156 150 20 20 0.0% 166 197 18.7% 37 1.8 81 4.0 Cleveland 138 143 6 4 -33.3% 37 24 -35.1% 23 3.8 5 0.8 Detroit 152 150 14 14 0.0% 138 182 31.9% 20 1.4 69 4.9 Grand Rapids 156 152 33 26 -21.2% 576 429 -25.5% 162 4.9 0 0.0 Hartford 176 168 15 13 -13.3% 370 283 -23.5% 92 6.1 0 0.0 Houma 133 133 10 9 -10.0% 38 27 -28.9% 12 1.2 0 0.0 Houston 152 152 34 24 -29.4% 294 215 -26.9% 91 2.7 1 0.0 Jackson 132 120 1 0 -100.0% 7 0 -100.0% 18 18.0 0 0.0 Lafayette 146 144 25 17 -32.0% 171 117 -31.6% 63 2.5 0 0.0 2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance Charlotte 2 2 0.0% Chicago 13 14 7.7% Cleveland 4 3 -25.0% Detroit 11 12 9.1% Grand Rapids 30 29 -3.3% Hartford 13 13 0.0% Houma 6 6 0.0% Houston 31 30 -3.2% Jackson 1 0 -100.0% Lafayette 15 15 0.0% IV-4-2 ------- June/July Episodes Peak (ppb) 2030 Base 2030 Control # Exceedances 2030 Base 2030 Control % Change Total Nonattainment (ppb) 2030 Base 2030 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) # Exceedances 2007 Base 2007 Control % Change 2030 Base Case vs 2030 Tier 2/Sulfur Control Case CMSA/MSA Louisville 137 134 5 3 -40.0% 26 22 -15.4% 18 3.6 8 1.6 Memphis 155 148 5 4 -20.0% 95 67 -29.5% 30 6.0 0 0.0 Milwaukee 136 136 4 3 -25.0% 28 23 -17.9% 15 3.8 4 1.0 Nashville 157 142 3 1 -66.7% 43 17 -60.5% 45 15.0 0 0.0 New London 163 154 18 16 -11.1% 312 207 -33.7% 109 6.1 0 0.0 New Orleans 155 154 43 31 -27.9% 358 271 -24.3% 106 2.5 0 0.0 New York City 184 177 152 117 -23.0% 2247 1695 -24.6% 780 5.1 95 0.6 Philadelphia 140 131 27 10 -63.0% 181 35 -80.7% 205 7.6 0 0.0 Pittsburgh 127 127 1 1 0.0% 2 2 0.0% 3 3.0 9 9.0 2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance Louisville 2 2 0.0% Memphis 5 4 -20.0% Milwaukee 3 3 0.0% Nashville 2 1 -50.0% New London 16 16 0.0% New Orleans 37 32 -13.5% New York City 123 118 -4.1% Philadelphia 19 19 0.0% Pittsburgh 0 0 0.0% IV-4-3 ------- June/July Episodes Peak (ppb) 2030 Base 2030 Control # Exceedances 2030 Base 2030 Control % Change Total Nonattainment (ppb) 2030 Base 2030 Control % Change Total ppb Decrease Decrease, on Average (ppb) Total ppb Increase Increase, on Average (ppb) # Exceedances 2007 Base 2007 Control % Change 2030 Base Case vs 2030 Tier 2/Sulfur Control Case CMSA/MSA Providence 152 142 15 11 -26.7% 193 92 -52.3% 112 7.5 0 0.0 Richmond 126 119 2 0 -100.0% 1 0 -100.0% 15 7.5 0 0.0 Rochester 127 125 2 2 0.0% 3 1 -66.7% 2 1.0 0 0.0 Rockford 133 131 2 1 -50.0% 11 6 -45.5% 7 3.5 0 0.0 Sarasota 153 142 3 2 -33.3% 42 19 -54.8% 29 9.7 0 0.0 St. Louis 139 131 11 2 -81.8% 61 7 -88.5% 116 10.5 0 0.0 Tampa 162 153 21 11 -47.6% 294 141 -52.0% 234 11.1 0 0.0 Toledo 133 133 1 1 0.0% 8 8 0.0% 0 0.0 0 0.0 Washington- Baltimore 154 146 45 25 -44.4% 442 165 -62.7% 363 8.1 3 0.1 York 125 117 1 0 -100.0% 0 0 0.0% 9 9.0 0 0.0 2007 Exceedances in June/July Episodes For Comparison to 2030 Exceedance Providence 13 11 -15.4% Richmond 0 0 0.0% Rochester 1 0 -100.0% Rockford 1 1 0.0% Sarasota 2 2 0.0% St. Louis 5 4 -20.0% Tampa 15 13 -13.3% Toledo 1 1 0.0% Washington- Baltimore 39 35 -10.3% York 0 0 0.0% IV-4-4 ------- Appendix IV-5: Limiting Modeled One-Hour Daily Maxima used in Calculation of Relative Reduction Factors As part of the identification of the need for the proposed Tier 2 standards, EPA is planning to use a rollback approach to link future-year model ozone changes to present-day ozone design values, thereby allowing an assessment of an area's future year nonattainment status. The specific equation used in the analysis is: DVF; = RRF; * DVQ where DVF; = the future design value predicted for site i, RRF; = the relative reduction factor calculated near site i, and DVC; = the current design value monitored at site i. One of the more important details in an accurate calculation of episode-average RRF; is to ensure that the calculated factor represents ozone improvements on high ozone days. If ozone predicted near a monitor on a particular day is very much less than the design value, the model predictions for that day could be unresponsive to controls (e.g., location could be upwind of controls for a given meteorological situation). EPA draft guidance on eight-hour attainment demonstrations recommended limiting RRF calculations to those instances where the daily maximum eight-hour model concentration in a nearby grid cell exceeded 70 ppb. This threshold was set based on 90 days of modeling data (at 158 sites) investigating the relationship between the RRF and the base magnitude. The Tier 2 modeling analyses will attempt to project future-year one-hour design values, therefore a separate rollback threshold will be needed. Two simple approaches were used to derive an appropriate cutoff. The first approach is based on the assumption that peak eight-hour ozone concentrations are generally 85 percent of their one-hour counterparts. Using this methodology, the 70 ppb threshold identified as part of the 8-hour analysis discussed above would translate to a 80-85 ppb value (82.4). The second approach looked at the relationship between one-hour model response (from a preliminary Tier 2 strategy run) and base model one-hour ozone at every grid cell of the domain over the July 8th - July 15th episode. As an example, Figure 1 shows a scatterplot of the two fields for July 14th for an across-the-board NOx simulation. There is a clear relationship between RRF and base ozone up to about 70-85 ppb. The largest reductions (RRFs of approximately 0.8) appear to occur in conjunction with base ozone values greater than about 80 ppb. Figure 2 shows the same style plot for a VOC control run. Again, the relationship between base ozone and ozone response appears to hold only to about 70-85 ppb. IV-5-1 ------- Figure 1. Scatterplot comparing base model ozone concentrations (x-axis) and relative reduction factor (y-axis) for each grid cell on July 14th, 1995. The control simulation was an across-the- board NOx simulation. Figure 2. Scatterplot comparing base model ozone concentrations (x-axis) and relative reduction factor (y-axis) for each grid cell on July 14th, 1995. The control simulation was an across-the- board VOC simulation. IV-5-2 ------- Appendix IV-6: Rollback Calculations 1995-1997 Design Values1 State Illinois Illinois Illinois Illinois Illinois Illinois Indiana Indiana Wisconsin Kentucky Kentucky Kentucky Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Texas Texas Texas Texas Texas Michigan Michigan Michigan County Cook Du Page Kane Lake McHenry Will Lake Porter Kenosha Boone Campbell Kenton Butler Clermont Hamilton Warren Ashtabula Cuyahoga Geauga Lake Lorain Medina Portage Summit Collin Dallas Denton Ellis Tarrant Genesee Lenawee Macomb 1995-97 Des Value 127 103 116 116 108 108 117 124 129 108 115 114 125 116 119 124 105 108 112 119 101 110 114 113 132 134 139 118 133 99 104 124 2007- B RRF 0.911 0.928 0.919 0.915 0.913 0.919 0.915 0.912 0.921 0.839 0.895 0.885 0.876 0.880 0.910 0.895 0.907 0.916 0.894 0.915 0.915 0.904 0.906 0.916 0.941 0.946 0.940 0.967 0.945 0.902 0.915 0.914 2007-B New DV 115 95 106 106 98 99 107 113 118 90 102 100 109 102 108 110 95 98 100 108 92 99 103 103 124 126 130 114 125 89 95 113 2007- C RRF 0.905 0.927 0.925 0.912 0.917 0.914 0.910 0.906 0.922 0.830 0.889 0.878 0.865 0.872 0.905 0.885 0.896 0.911 0.883 0.906 0.910 0.892 0.893 0.906 0.930 0.937 0.925 0.946 0.930 0.891 0.907 0.918 2007-C New DV 114 95 107 105 99 98 106 112 118 89 102 100 108 101 107 109 94 98 98 107 91 98 101 102 122 125 128 111 123 88 94 113 CMSA Name (if applicable) Chicago Chicago Chicago Chicago Chicago Chicago Chicago Chicago Chicago Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Dallas Dallas Dallas Dallas Dallas Detroit Detroit Detroit MSA Name (if applicable) CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL GARY, IN GARY, IN KENOSHA, Wl CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN HAMILTON, OH CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CLEVELAND CLEVELAND CLEVELAND CLEVELAND CLEVELAND CLEVELAND AKRON, OH AKRON, OH DALLAS, TX DALLAS, TX DALLAS, TX DALLAS, TX FORT WORTH, TX FLINT, Ml ANN ARBOR, Ml DETROIT, Ml i *** are below 80 ppb. county where RRF is not defined because all current 1-hr daily maximum model ozone values (for all 30 days) IV-6-1 ------- Michigan Michigan Michigan Michigan Texas Texas Texas Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Connecticut Connecticut New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New York New York New York New York New York New York New York New York New York New York Delaware Maryland New Jersey New Jersey New Jersey New Jersey Pennsylvania Pennsylvania Pennsylvania Pennsylvania D.C. Oakland St Clair Washtenaw Wayne Brazoria Galveston Harris Milwaukee Ozaukee Racine Washington Waukesha Fairfield New Haven Bergen Essex Hudson Hunterdon Mercer Middlesex Monmouth Morris Ocean Union Bronx Out chess Kings New York Orange Putnam Queens Richmond Suffolk Westchester New Castle Cecil Atlantic Camden Cumberland Gloucester Bucks Delaware Montgomery Philadelphia Washington 117 119 104 114 148 182 189 126 127 119 106 109 138 157 122 114 120 119 131 139 138 124 149 109 123 113 124 121 115 122 125 137 138 121 139 152 124 137 115 128 137 126 122 130 125 0.913 0.924 0.924 0.923 0.975 0.968 0.944 0.918 0.919 0.921 0.895 0.900 0.937 0.935 0.954 0.938 0.938 0.921 0.917 0.921 0.934 0.898 0.908 0.924 0.954 0.799 0.926 0.926 0.860 0.893 0.924 0.934 0.923 0.941 0.849 0.842 0.909 0.912 0.869 0.885 0.910 0.882 0.890 0.903 0.906 106 109 96 105 144 176 178 115 116 109 94 98 129 146 116 106 112 109 120 128 128 111 135 100 117 90 114 112 98 108 115 127 127 113 118 127 112 124 99 113 124 111 108 117 113 0.922 0.922 0.917 0.925 0.968 0.961 0.944 0.914 0.915 0.921 0.892 0.897 0.934 0.930 0.954 0.934 0.934 0.913 0.911 0.916 0.927 0.889 0.899 0.920 0.954 0.794 0.921 0.921 0.858 0.891 0.917 0.932 0.918 0.938 0.838 0.831 0.899 0.901 0.859 0.877 0.904 0.874 0.886 0.899 0.902 107 109 95 105 143 174 178 115 116 109 94 97 128 146 116 106 112 108 119 127 127 110 133 100 117 89 114 111 98 108 114 127 126 113 116 126 111 123 98 112 123 110 108 116 112 Detroit Detroit Detroit Detroit Houston Houston Houston Milwaukee Milwaukee Milwaukee Milwaukee Milwaukee New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Washington- Baltimore DETROIT, Ml DETROIT, Ml ANN ARBOR, Ml DETROIT, Ml BRAZORIA, TX GALVESTON, TX HOUSTON, TX MILWAUKEE, Wl MILWAUKEE, Wl RACINE, Wl MILWAUKEE, Wl MILWAUKEE, Wl NEW HAVEN, CT NEW HAVEN, CT BERGEN, NJ NEWARK, NJ JERSEY CITY, NJ MIDDLESEX, NJ TRENTON, NJ MIDDLESEX, NJ MONMOUTH, NJ NEWARK, NJ MONMOUTH, NJ NEWARK, NJ NEW YORK, NY DUTCHESS COUNTY, NY NEW YORK, NY NEW YORK, NY NEWBURGH, NY-PA NEW YORK, NY NEW YORK, NY NEW YORK, NY NASSAU-SUFFOLK, NY NEW YORK, NY WILMINGTON, DE-MD WILMINGTON, DE-MD ATLANTIC-CAPE MAY, NJ PHILADELPHIA, PA-NJ VINELAND, NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ WASHINGTON, DC IV-6-2 ------- Maryland Maryland Maryland Maryland Maryland Maryland Maryland Maryland Maryland Virginia Virginia Virginia Virginia Virginia Virginia New York New York New York Pennsylvania Pennsylvania Pennsylvania Wisconsin Wisconsin North Carolina Georgia Georgia Georgia Georgia Georgia Georgia Georgia South Carolina Anne Arundel Baltimore Baltimore City Calvert Carroll Charles Harford Montgomery Prince Georges Alexandria City Arlington Fairfax Fauquier Prince William Stafford Albany Saratoga Schenectady Lehigh Northampton Blair Outagamie Winnebago Buncombe De Kalb Douglas Fulton Gwinnett Paulding Rockdale Richmond Aiken 142 130 137 105 115 118 145 118 132 124 123 124 97 110 110 105 101 94 114 109 114 98 98 86 136 140 143 121 112 145 118 104 0.892 0.890 0.899 0.848 0.882 0.857 0.881 0.892 0.892 0.909 0.909 0.904 0.871 0.877 0.879 0.833 0.846 0.815 0.884 0.893 0.851 0.864 0.902 0.825 0.894 0.902 0.899 0.894 0.849 0.875 0.886 0.861 126 115 123 89 101 101 127 105 117 112 111 112 84 96 96 87 85 76 100 97 97 84 88 70 121 126 128 108 95 126 104 89 0.883 0.880 0.892 0.835 0.871 0.844 0.871 0.884 0.883 0.906 0.906 0.896 0.860 0.867 0.867 0.819 0.829 0.808 0.877 0.883 0.839 0.854 0.892 0.803 0.872 0.882 0.876 0.860 0.826 0.839 0.857 0.841 125 114 122 87 100 99 126 104 116 112 111 111 83 95 95 86 83 75 99 96 95 83 87 69 118 123 125 104 92 121 101 87 Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore BALTIMORE.MD BALTIMORE.MD BALTIMORE.MD WASHINGTON, DC BALTIMORE.MD WASHINGTON, DC BALTIMORE.MD WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC ALBANY, NY ALBANY, NY ALBANY, NY ALLENTOWN, PA ALLENTOWN, PA ALTOONA, PA APPLETON, Wl APPLETON, Wl ASHEVILLE, NC ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA AUGUSTA, GA-SC AUGUSTA, GA-SC IV-6-3 ------- South Carolina Texas Maine Massachusetts Louisiana Louisiana Louisiana Louisiana Texas Texas Michigan Mississippi Mississippi Alabama Alabama Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts New Hampshire New York New York Vermont Ohio Iowa Illinois West Virginia South Carolina South Carolina North Carolina North Carolina North Carolina South Carolina Tennessee Kentucky South Carolina Georgia Ohio Ohio Ohio Ohio Texas Illinois Iowa Edgefield Travis Penobscot Barnstable Ascension East Baton Rouge Livingston West Baton Rouge Jefferson Orange Berrien Hancock Jackson Jefferson Shelby Bristol Essex Middlesex Plymouth Suffolk Worcester Rocking ham Erie Niagara Chittenden Stark Linn Champaign Kanawha Berkeley Charleston Lincoln Mecklenburg Rowan York Hamilton Christian Richland Muscogee Delaware Franklin Licking Madison Nueces Rock Island Scott 93 104 95 131 121 131 127 114 139 121 119 105 109 132 127 138 113 109 102 95 108 130 91 102 85 107 74 94 110 94 102 105 123 119 114 113 101 107 108 99 107 115 112 115 83 95 0.851 0.936 0.942 0.911 0.990 0.981 0.988 0.979 0.981 0.987 0.907 0.985 0.998 0.879 0.882 0.883 0.954 0.917 0.909 0.912 0.910 0.959 0.922 0.920 *** 0.908 0.936 0.869 0.891 0.900 0.873 0.872 0.876 0.846 0.874 0.862 0.751 0.893 0.900 0.881 0.897 0.878 0.872 *** 0.924 0.920 79 97 89 119 119 128 125 111 136 119 107 103 108 116 112 121 107 99 92 86 98 124 83 93 *** 97 69 81 97 84 89 91 107 100 99 97 75 95 97 87 95 100 97 *** 76 87 0.827 0.919 0.932 0.900 0.982 0.970 0.981 0.965 0.975 0.979 0.897 0.975 0.988 0.857 0.860 0.872 0.945 0.908 0.897 0.902 0.900 0.951 0.916 0.911 *** 0.893 0.927 0.857 0.882 0.876 0.852 0.851 0.855 0.826 0.852 0.829 0.742 0.861 0.867 0.869 0.894 0.864 0.861 *** 0.920 0.915 76 95 88 117 118 127 124 110 135 118 106 102 107 113 109 120 106 98 91 85 97 123 83 92 *** 95 68 80 96 82 86 89 105 98 97 93 74 92 93 86 95 99 96 *** 76 86 AUGUSTA, GA-SC AUSTIN, TX BANGOR, ME BARNSTABLE, MA BATON ROUGE, LA BATON ROUGE, LA BATON ROUGE, LA BATON ROUGE, LA BEAUMONT, TX BEAUMONT, TX BENTON HARBOR, Ml BILOXI, MS BILOXI, MS BIRMINGHAM, AL BIRMINGHAM, AL BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BUFFALO, NY BUFFALO, NY BURLINGTON, VT CANTON, OH CEDAR RAPIDS, I A CHAMPAIGN, IL CHARLESTON, WV CHARLESTON, SC CHARLESTON, SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHATTANOOGA, TN-GA CLARKSVILLE, TN-KY COLUMBIA, SC COLUMBUS, GA-AL COLUMBUS, OH COLUMBUS, OH COLUMBUS, OH COLUMBUS, OH CORPUS CHRISTI, TX DAVENPORT, IA-IL DAVENPORT, IA-IL IV-6-4 ------- Florida Ohio Ohio Ohio Ohio Alabama Alabama Illinois Iowa Iowa Delaware Indiana New York Pennsylvania Indiana Indiana Indiana Kentucky North Carolina Alabama Florida Florida Indiana Indiana Florida Michigan Michigan Michigan Michigan Wisconsin North Carolina North Carolina North Carolina North Carolina South Carolina South Carolina South Carolina South Carolina Pennsylvania Pennsylvania Connecticut Connecticut Connecticut Connecticut North Carolina North Carolina Louisiana Volusia Clark Greene Miami Montgomery Lawrence Morgan Macon Polk Warren Kent Elkhart Chemung Erie Posey Vanderburgh Warrick Henderson Cumberland Colbert Lee St Lucie Allen De Kalb Alachua Allegan Kent Muskegon Ottawa Brown Da vie Forsyth Guilford Pitt Anderson Cherokee Pickens Spartanburg Dauphin Perry Hartford Litchfield Middlesex Tolland Alexander Caldwell Lafourche 89 118 111 110 112 98 114 100 82 74 124 113 88 105 99 114 113 108 106 83 83 82 106 82 101 137 124 136 113 108 105 115 109 104 114 106 107 117 113 103 138 120 135 127 94 97 127 0.955 0.876 0.865 0.875 0.874 0.813 0.876 0.859 0.889 0.881 0.901 0.884 0.900 0.908 0.875 0.873 0.865 0.865 0.878 0.812 0.989 0.997 0.906 0.904 0.923 0.917 0.910 0.919 0.919 0.898 0.809 0.839 0.864 0.900 0.870 0.860 0.847 0.853 0.901 0.844 0.923 0.897 0.939 0.919 0.822 0.869 0.987 85 103 95 96 97 79 99 85 72 65 111 99 79 95 86 99 97 93 93 67 82 81 96 74 93 125 112 124 103 97 84 96 94 93 99 91 90 99 101 86 127 107 126 116 77 84 125 0.928 0.863 0.852 0.860 0.864 0.797 0.861 0.846 0.874 0.867 0.887 0.873 0.884 0.897 0.865 0.862 0.855 0.856 0.851 0.796 0.966 0.975 0.893 0.890 0.900 0.911 0.904 0.912 0.913 0.889 0.790 0.815 0.840 0.882 0.846 0.840 0.823 0.829 0.881 0.825 0.917 0.888 0.933 0.910 0.807 0.847 0.980 82 101 94 94 96 78 98 84 71 64 110 98 77 94 85 98 96 92 90 66 80 79 94 72 90 124 112 124 103 95 82 93 91 91 96 89 88 96 99 85 126 106 126 115 75 82 124 DAYTONA BEACH, FL DAYTON, OH DAYTON, OH DAYTON, OH DAYTON, OH DECATUR, AL DECATUR, AL DECATUR, IL DES MOINES, IA DES MOINES, IA DOVER, DE ELKHART, IN ELMIRA, NY ERIE, PA EVANSVILLE, IN-KY EVANSVILLE, IN-KY EVANSVILLE, IN-KY EVANSVILLE, IN-KY FAYETTEVILLE, NC FLORENCE, AL FORT MEYERS, FL FORT PIERCE-, FL FORT WAYNE, IN FORT WAYNE, IN GAINESVILLE, FL GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GREEN BAY, Wl GREENSBORO, NC GREENSBORO, NC GREENSBORO, NC GREENVILLE, NC GREENVILLE, SC GREENVILLE, SC GREENVILLE, SC GREENVILLE, SC HARRISBURG, PA HARRISBURG, PA HARTFORD, CT HARTFORD, CT HARTFORD, CT HARTFORD, CT HICKORY, NC HICKORY, NC HOUMA, LA IV-6-5 ------- Kentucky Kentucky Ohio West Virginia Alabama Indiana Indiana Indiana Indiana Indiana Indiana Mississippi Mississippi Tennessee Florida Florida New York Wisconsin Tennessee Pennsylvania Michigan Kansas Missouri Missouri Missouri Tennessee Tennessee Tennessee Tennessee Tennessee Indiana Louisiana Louisiana Florida Pennsylvania Michigan Michigan Kentucky Kentucky Kentucky Ohio Nebraska Arkansas Texas Indiana Indiana Kentucky Boyd Greenup Lawrence Cabell Madison Hamilton Hancock Johnson Madison Marion Morgan Hinds Madison Madison Duval St Johns Chautauqua Rock Sullivan Cambria Kalamazoo Wyandotte Clay Jackson Platte Anderson Blount Knox Loudon Sevier Tippecanoe Lafayette Calcasieu Polk Lancaster Clinton Ingham Fayette Jessamine Scott Allen Lancaster Pulaski Gregg Clark Floyd Bullitt 122 114 113 122 102 116 120 102 112 115 103 97 89 64 116 91 104 103 111 102 106 113 128 88 116 110 117 120 112 111 104 109 116 99 125 88 97 101 98 101 106 68 108 139 125 125 116 0.858 0.842 0.837 0.862 0.871 0.900 0.902 0.863 0.901 0.904 0.887 0.944 0.968 0.826 0.965 0.953 0.910 0.898 0.861 0.875 0.897 0.950 0.947 0.933 0.946 0.834 0.840 0.866 0.846 0.796 0.891 0.974 0.987 0.972 0.895 0.914 0.916 0.885 0.878 0.831 0.892 0.938 0.952 0.977 0.889 0.895 0.874 104 95 94 105 88 104 108 88 100 103 91 91 86 52 111 86 94 92 95 89 95 107 121 82 109 91 98 103 94 88 92 106 114 96 111 80 88 89 86 83 94 63 102 135 111 111 101 0.847 0.832 0.826 0.851 0.848 0.889 0.890 0.853 0.888 0.896 0.881 0.921 0.960 0.811 0.936 0.925 0.899 0.890 0.847 0.864 0.886 0.941 0.935 0.923 0.937 0.813 0.816 0.844 0.824 0.778 0.879 0.964 0.979 0.947 0.882 0.904 0.907 0.872 0.866 0.819 0.880 0.925 0.927 0.960 0.883 0.891 0.868 103 94 93 103 86 103 106 87 99 103 90 89 85 51 108 84 93 91 94 88 93 106 119 81 108 89 95 101 92 86 91 105 113 93 110 79 87 88 84 82 93 62 100 133 110 111 100 HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTSVILLE, AL INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN JACKSON, MS JACKSON, MS JACKSON, TN JACKSONVILLE, FL JACKSONVILLE, FL JAMESTOWN, NY JANESVILLE-BELOIT, Wl JOHNSON CITY, TN-VA JOHNSTOWN, PA KALAMAZOO, Ml KANSAS CITY, MO-KS KANSAS CITY, MO-KS KANSAS CITY, MO-KS KANSAS CITY, MO-KS KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN LAFAYETTE, IN LAFAYETTE, LA LAKE CHARLES, LA LAKELAND, FL LANCASTER, PA LANSING, Ml LANSING, Ml LEXINGTON, KY LEXINGTON, KY LEXINGTON, KY LIMA, OH LINCOLN, NE LITTLE ROCK, AR LONGVIEW, TX LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOUISVILLE, KY-IN IV-6-6 ------- Kentucky Kentucky New Hampshire Georgia Wisconsin New Hampshire Florida Arkansas Mississippi Tennessee Minnesota Minnesota Minnesota Wisconsin Alabama Louisiana Alabama Alabama Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Connecticut Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana Virginia Virginia Oklahoma Oklahoma Oklahoma Nebraska Florida Florida Florida Kentucky Ohio West Virginia Florida Illinois Pennsylvania Pennsylvania Jefferson Oldham Hillsborough Bibb Dane Merrimack Brevard Crittenden De Soto Shelby Anoka Dakota Washington St Croix Mobile Ouachita Elmore Montgomery Davidson Dickson Rutherford Sumner Williamson Wilson New London Jefferson Orleans St Bernard St Charles St James St John The Baptis Hampton City Suffolk City Cleveland Me Clain Oklahoma Douglas Orange Osceola Seminole Daviess Washington Wood Escambia Peoria Allegheny Beaver 120 112 111 122 97 98 86 122 131 128 106 91 103 88 111 94 102 92 110 120 95 124 110 108 144 107 96 98 115 119 114 109 108 102 95 110 88 106 96 95 108 110 116 113 95 133 105 0.892 0.871 0.902 0.808 0.921 0.864 0.986 0.914 0.941 0.943 0.913 0.918 0.948 0.938 0.920 0.976 0.894 0.891 0.908 0.691 0.870 0.887 0.813 0.858 0.930 0.970 0.969 0.982 0.978 0.990 0.982 0.912 0.908 0.972 0.993 0.977 0.926 0.985 0.983 0.975 0.846 0.843 0.830 0.967 0.899 0.895 0.932 107 97 100 98 89 84 84 111 123 120 96 83 97 82 102 91 91 81 99 82 82 110 89 92 133 103 93 96 112 117 111 99 98 99 94 107 81 104 94 92 91 92 96 109 85 119 97 0.888 0.864 0.895 0.783 0.909 0.858 0.960 0.904 0.928 0.935 0.912 0.915 0.938 0.924 0.904 0.965 0.871 0.867 0.889 0.681 0.846 0.869 0.795 0.841 0.924 0.962 0.963 0.975 0.972 0.982 0.975 0.904 0.900 0.952 0.972 0.959 0.915 0.956 0.955 0.945 0.838 0.833 0.821 0.949 0.890 0.885 0.921 106 96 99 95 88 84 82 110 121 119 96 83 96 81 100 90 88 79 97 81 80 107 87 90 133 102 92 95 111 116 111 98 97 97 92 105 80 101 91 89 90 91 95 107 84 117 96 LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOWELL, MA-NH MACON, GA MADISON, Wl MANCHESTER, NH MELBOURNE, FL MEMPHIS, TN-AR-MS MEMPHIS, TN-AR-MS MEMPHIS, TN-AR-MS MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MOBILE, AL MONROE, LA MONTGOMERY, AL MONTGOMERY, AL NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NEW LONDON, CT NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NORFOLK, VA NORFOLK, VA OKLAHOMA CITY, OK OKLAHOMA CITY, OK OKLAHOMA CITY, OK OMAHA, NE-IA ORLANDO, FL ORLANDO, FL ORLANDO, FL OWENSBORO, KY PARKERSBURG, WV-OH PARKERSBURG, WV-OH PENSACOLA, FL PEORIA-PEKIN, IL PITTSBURGH, PA PITTSBURGH, PA IV-6-7 ------- Pennsylvania Pennsylvania Massachusetts Maine Maine New Hampshire Rhode Island Rhode Island Rhode Island North Carolina North Carolina North Carolina North Carolina North Carolina Pennsylvania Virginia Virginia Virginia Virginia Virginia New York New York Illinois North Carolina Florida Florida Georgia Pennsylvania Pennsylvania Pennsylvania Wisconsin Louisiana Louisiana Indiana Illinois Massachusetts Massachusetts Missouri Illinois Illinois Illinois Missouri Missouri Missouri Missouri Pennsylvania Ohio Washington Westmoreland Berkshire Cumberland York Strafford Kent Providence Washington Chatham Durham Franklin Johnston Wake Berks Charles City Chesterfield Hanover Henrico Roanoke Monroe Wayne Winnebago Edgecombe Manatee Sarasota Chatham Lackawanna Luzerne Mercer Sheboygan Bossier Caddo St Joseph Sangamon Hampden Hampshire Greene Jersey Madison St Clair Jefferson St Charles St Louis St Louis City Centre Jefferson 117 125 89 121 126 101 133 117 113 103 103 110 107 114 118 119 114 124 115 94 102 102 93 102 96 99 85 110 110 111 123 98 101 114 98 126 132 101 112 128 108 125 131 119 108 118 111 0.876 0.897 0.880 0.931 0.959 0.917 0.918 0.926 0.909 0.856 0.883 0.868 0.890 0.913 0.876 0.872 0.881 0.882 0.892 0.846 0.923 0.927 0.905 0.908 0.974 0.968 0.924 0.853 0.840 0.885 0.927 0.967 0.967 0.896 0.857 0.906 0.897 0.815 0.896 0.922 0.930 0.915 0.922 0.924 0.927 0.861 0.902 102 112 78 112 120 92 122 108 102 88 90 95 95 104 103 103 100 109 102 79 94 94 84 92 93 95 78 93 92 98 114 94 97 102 84 114 118 82 100 118 100 114 120 109 100 101 100 0.866 0.887 0.869 0.923 0.951 0.911 0.911 0.917 0.900 0.835 0.862 0.846 0.866 0.891 0.864 0.859 0.868 0.866 0.877 0.828 0.913 0.918 0.898 0.893 0.952 0.944 0.905 0.838 0.825 0.868 0.923 0.950 0.950 0.886 0.842 0.898 0.890 0.792 0.875 0.902 0.912 0.896 0.900 0.905 0.911 0.845 0.892 101 110 77 111 119 91 121 107 101 85 88 93 92 101 101 102 98 107 100 77 93 93 83 91 91 93 76 92 90 96 113 93 95 100 82 113 117 80 98 115 98 112 117 107 98 99 98 PITTSBURGH, PA PITTSBURGH, PA PITTSFIELD, MA PORTLAND, ME PORTLAND, ME PORTSMOUTH, NH-ME PROVIDENCE, RI-MA PROVIDENCE, RI-MA PROVIDENCE, RI-MA RALEIGH, NC RALEIGH, NC RALEIGH, NC RALEIGH, NC RALEIGH, NC READING, PA RICHMOND, VA RICHMOND, VA RICHMOND, VA RICHMOND, VA ROANOKE, VA ROCHESTER, NY ROCHESTER, NY ROCKFORD, IL ROCKY MOUNT, NC SARASOTA, FL SARASOTA, FL SAVANNAH, GA SCRANTON, PA SCRANTON, PA SHARON, PA SHEBOYGAN, Wl SHREVEPORT, LA SHREVEPORT, LA SOUTH BEND, IN SPRINGFIELD, IL SPRINGFIELD, MA SPRINGFIELD, MA SPRINGFIELD, MO ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL STATE COLLEGE, PA STEUBENVILLE, OH-WV IV-6-8 ------- West Virginia New York New York Florida Florida Florida Florida Indiana Ohio Ohio Oklahoma Texas New York New York Texas Wisconsin Florida West Virginia Kansas Pennsylvania North Carolina Pennsylvania Ohio Ohio Alabama Alabama Alabama Arkansas Arkansas Delaware Georgia Georgia Georgia Georgia Illinois Illinois Illinois Illinois Illinois Indiana Indiana Indiana Iowa Iowa Iowa Iowa Kentucky Hancock Madison Onondaga Leon Hillsborough Pasco Pinellas Vigo Lucas Wood Tulsa Smith Herkimer Oneida Victoria Marathon Palm Beach Ohio Sedgwick Lycoming New Hanover York Ma honing Trumbull Clay Geneva Sumter Montgomery Newton Sussex Dawson Fannin Glynn Sumter Adams Effing ham Hamilton Macoupin Randolph Kosciusko La Porte Lawrence Harrison Palo Alto Story Van Buren Bell 106 89 102 96 112 92 93 107 111 94 121 109 88 95 95 84 89 107 96 91 102 109 109 109 110 84 83 79 83 123 97 92 89 98 89 97 89 102 94 100 146 100 79 70 87 82 92 0.902 0.938 0.911 0.928 0.967 0.965 0.962 0.889 0.915 0.914 0.977 0.955 0.954 0.906 *** 0.893 0.979 0.869 0.961 0.880 0.921 0.889 0.891 0.884 0.817 0.893 0.813 *** *** 0.880 0.862 0.855 0.920 0.881 0.895 0.838 0.830 0.856 0.841 0.900 0.924 0.823 0.924 *** 0.887 0.899 0.795 95 83 92 89 108 88 89 95 101 85 118 104 83 86 *** 75 87 92 92 80 93 96 97 96 89 74 67 *** *** 108 83 78 81 86 79 81 73 87 79 89 134 82 73 *** 77 73 73 0.892 0.925 0.899 0.900 0.953 0.942 0.950 0.878 0.910 0.905 0.964 0.938 0.949 0.892 *** 0.883 0.947 0.858 0.948 0.863 0.905 0.875 0.875 0.870 0.797 0.869 0.801 *** *** 0.867 0.832 0.823 0.901 0.856 0.887 0.825 0.821 0.837 0.830 0.888 0.919 0.811 0.913 *** 0.873 0.890 0.776 94 82 91 86 106 86 88 93 100 85 116 102 83 84 *** 74 84 91 91 78 92 95 95 94 87 73 66 *** *** 106 80 75 80 83 78 80 73 85 77 88 134 81 72 *** 75 72 71 STEUBENVILLE, OH-WV SYRACUSE, NY SYRACUSE, NY TALLAHASSEE, FL TAMPA, FL TAMPA, FL TAMPA, FL TERRE HAUTE, IN TOLEDO, OH TOLEDO, OH TULSA, OK TYLER, TX UTICA-ROME, NY UTICA-ROME, NY VICTORIA, TX WAUSAU, Wl WEST PALM BEACH, FL WHEELING, WV-OH WICHITA, KS WILLIAMSPORT, PA WILMINGTON, NC YORK, PA YOUNGSTOWN, OH YOUNGSTOWN, OH IV-6-9 ------- Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Louisiana Louisiana Louisiana Louisiana Louisiana Maine Maine Maine Maine Maine Maine Maine Maryland Michigan Michigan Michigan Michigan Michigan Michigan Mississippi Mississippi Mississippi Mississippi Mississippi Mississippi Mississippi Missouri Missouri New Hampshire New Hampshire New Hampshire New Hampshire New Hampshire New Hampshire Edmonson Graves Hancock Hardin Lawrence Livingston McCracken McLean Perry Pike Pulaski Simpson Trigg Beauregard Grant Iberville Pointe Coupee St Mary Hancock Kennebec Knox Oxford Piscataquis Sagadahoc Somerset Kent Benzie Cass Huron Mason Mecosta Roscommon Adams Choctaw Franklin Lauderdale Lee Sharkey Warren Monroe Ste Genevieve Belknap Carroll Cheshire Coos Grafton Sullivan 113 92 114 113 95 108 100 103 90 98 94 101 106 117 91 139 111 104 121 98 119 79 80 125 92 129 108 115 110 125 110 99 97 81 94 92 96 95 97 96 108 89 88 91 93 77 90 0.792 0.861 0.820 0.827 0.798 0.841 0.855 0.856 0.766 0.782 0.832 0.817 0.805 0.978 0.974 0.990 0.976 0.990 0.916 0.950 0.916 0.911 *** 0.933 0.958 0.874 0.933 0.890 0.931 0.919 0.911 0.916 0.968 0.858 0.965 0.848 0.828 0.954 0.977 0.870 0.875 0.814 0.858 0.921 0.847 0.831 0.906 89 79 93 93 75 90 85 88 68 76 78 82 85 114 88 137 108 102 110 93 108 71 *** 116 88 112 100 102 102 114 100 90 93 69 90 78 79 90 94 83 94 72 75 83 78 63 81 0.781 0.853 0.811 0.818 0.789 0.833 0.847 0.847 0.754 0.768 0.818 0.805 0.796 0.969 0.961 0.983 0.963 0.984 0.906 0.942 0.906 0.905 *** 0.924 0.946 0.862 0.924 0.879 0.923 0.912 0.901 0.907 0.957 0.842 0.954 0.828 0.812 0.945 0.968 0.859 0.860 0.809 0.855 0.910 0.842 0.824 0.897 88 78 92 92 74 89 84 87 67 75 76 81 84 113 87 136 106 102 109 92 107 71 *** 115 87 111 99 101 101 113 99 89 92 68 89 76 77 89 93 82 92 71 75 82 78 63 80 IV-6-10 ------- New York New York New York New York North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina Ohio Ohio Ohio Ohio Ohio Oklahoma Oklahoma Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Vermont Essex Hamilton Jefferson Ulster Camden Caswell Duplin Granville Haywood Martin Northampton Person Rocking ham Swain Yancey Clinton Knox Logan Preble Union Latimer Okmulgee Armstrong Clearfield Franklin Greene Lawrence Monroe Abbeville Barnwell Chester Colleton Darlington Oconee Union Williamsburg Bradley Coffee Dyer Giles Hamblen Haywood Humphreys Jefferson Lawrence Putnam Bennington 101 97 110 97 93 111 89 116 103 90 100 100 113 78 108 121 113 100 110 75 100 94 90 116 113 123 101 117 93 99 107 91 94 92 98 85 106 105 112 104 96 97 102 125 93 99 99 0.951 0.813 0.928 0.834 0.920 0.854 0.862 0.875 0.803 0.913 0.846 0.871 0.860 0.790 0.867 0.846 0.893 0.886 0.867 0.875 0.960 0.975 0.890 0.872 0.835 0.796 0.901 0.881 0.883 0.838 0.878 0.862 0.886 0.838 0.816 0.823 0.771 0.836 0.844 0.752 0.798 0.881 0.777 0.803 0.713 0.822 0.911 96 78 102 80 85 94 76 101 82 82 84 87 97 61 93 102 100 88 95 65 96 91 80 101 94 97 90 103 82 82 93 78 83 77 79 69 81 87 94 78 76 85 79 100 66 81 90 0.945 0.804 0.922 0.825 0.910 0.835 0.843 0.854 0.784 0.898 0.832 0.853 0.837 0.771 0.845 0.833 0.881 0.872 0.852 0.862 0.949 0.961 0.879 0.860 0.820 0.785 0.885 0.873 0.862 0.819 0.856 0.843 0.861 0.809 0.797 0.808 0.751 0.817 0.833 0.737 0.784 0.869 0.769 0.785 0.699 0.804 0.901 95 78 101 80 84 92 75 99 80 80 83 85 94 60 91 100 99 87 93 64 94 90 79 99 92 96 89 102 80 81 91 76 80 74 78 68 79 85 93 76 75 84 78 98 65 79 89 IV-6-11 ------- Virginia Virginia Virginia Virginia Virginia West Virginia Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Caroline Frederick Henry Madison Wythe Greenbrier Columbia Dodge Door Florence Fond Du Lac Jefferson Kewaunee Manitowoc Oneida Polk Sauk Vernon Walworth 109 102 101 99 94 99 104 93 127 80 96 94 121 145 78 85 90 85 100 0.873 0.824 0.813 0.790 0.774 0.731 0.918 0.877 0.945 *** 0.886 0.893 0.920 0.919 *** 0.929 0.883 0.920 0.894 95 84 82 78 72 72 95 81 120 *** 85 83 111 133 *** 78 79 78 89 0.858 0.811 0.796 0.777 0.760 0.719 0.906 0.869 0.936 *** 0.878 0.884 0.911 0.915 *** 0.915 0.872 0.909 0.888 93 82 80 76 71 71 94 80 118 *** 84 83 110 132 *** 77 78 77 88 IV-6-12 ------- 1996-1998 Design Values State Illinois Illinois Illinois Illinois Illinois Illinois Indiana Indiana Wisconsin Kentucky Kentucky Kentucky Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Ohio Texas Texas Texas Texas Texas Michigan Michigan Michigan Michigan Michigan Michigan Michigan Texas Texas Texas Wisconsin Wisconsin County Cook Du Page Kane Lake McHenry Will Lake Porter Kenosha Boone Campbell Kenton Butler Clermont Hamilton Warren Ashtabula Cuyahoga Geauga Lake Lorain Medina Portage Summit Collin Dallas Denton Ellis Tarrant Genesee Lenawee Macomb Oakland St Clair Washtenaw Wayne Brazoria Galveston Harris Milwaukee Ozaukee 1996-98 Des Value 125 97 92 124 94 95 113 124 136 108 115 120 118 116 124 124 108 106 116 123 101 105 110 112 128 135 135 130 128 104 99 123 100 118 99 114 134 170 196 129 129 2007-B RRF 0.920 0.928 0.919 0.921 0.913 0.891 0.912 0.912 0.921 0.839 0.895 0.885 0.876 0.880 0.910 0.895 0.907 0.901 0.894 0.915 0.915 0.904 0.906 0.916 0.941 0.947 0.940 0.967 0.945 0.913 0.915 0.914 0.913 0.913 0.924 0.923 0.975 0.971 0.946 0.918 0.919 2007-B New DV 114 90 84 114 85 84 103 113 125 90 102 106 103 102 112 110 97 95 103 112 92 94 99 102 120 127 126 125 120 94 90 112 91 107 91 105 130 165 185 118 118 2007-C RRF 0.921 0.927 0.925 0.922 0.917 0.883 0.906 0.906 0.922 0.830 0.889 0.878 0.865 0.872 0.905 0.885 0.896 0.892 0.883 0.906 0.910 0.892 0.893 0.906 0.930 0.937 0.925 0.946 0.932 0.901 0.907 0.918 0.922 0.908 0.917 0.925 0.968 0.964 0.946 0.914 0.915 2007-C New DV 115 89 85 114 86 83 102 112 125 89 102 105 102 101 112 109 96 94 102 111 91 93 98 101 119 126 124 122 119 93 89 112 92 107 90 105 129 163 185 117 118 CMSA Name (if applicable) Chicago Chicago Chicago Chicago Chicago Chicago Chicago Chicago Chicago Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cincinnati Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Cleveland Dallas Dallas Dallas Dallas Dallas Detroit Detroit Detroit Detroit Detroit Detroit Detroit Houston Houston Houston Milwaukee Milwaukee MSA Name (if applicable) CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL CHICAGO, IL GARY, IN GARY, IN KENOSHA, Wl CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN HAMILTON, OH CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CINCINNATI, OH-KY-IN CLEVELAND CLEVELAND CLEVELAND CLEVELAND CLEVELAND CLEVELAND AKRON, OH AKRON, OH DALLAS, TX DALLAS, TX DALLAS, TX DALLAS, TX FORT WORTH, TX FLINT, Ml ANN ARBOR, Ml DETROIT, Ml DETROIT, Ml DETROIT, Ml ANN ARBOR, Ml DETROIT, Ml BRAZORIA, TX GALVESTON, TX HOUSTON, TX MILWAUKEE, Wl MILWAUKEE, Wl IV-6-13 ------- Wisconsin Wisconsin Wisconsin Connecticut Connecticut New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New Jersey New York New York New York New York New York New York New York New York New York New York Delaware Maryland New Jersey New Jersey New Jersey New Jersey Pennsylvania Pennsylvania Pennsylvania Pennsylvania D.C. Maryland Maryland Maryland Maryland Maryland Racine Washington Waukesha Fairfield New Haven Bergen Essex Hudson Hunterdon Mercer Middlesex Monmouth Morris Ocean Passaic Union Bronx Dutchess Kings New York Orange Putnam Queens Richmond Suffolk Westchester New Castle Cecil Atlantic Camden Cumberland Gloucester Bucks Delaware Montgomery Philadelphia Washington Anne Arundel Baltimore Baltimore City Calvert Carroll 129 106 103 134 139 116 112 120 119 121 132 129 116 139 120 109 122 111 114 121 115 121 141 138 137 115 127 152 124 129 115 122 119 126 126 125 118 138 126 137 112 115 0.921 0.895 0.900 0.937 0.935 0.954 0.938 0.938 0.921 0.917 0.921 0.934 0.898 0.908 0.918 0.924 0.954 0.799 0.926 0.926 0.860 0.893 0.924 0.934 0.923 0.941 0.849 0.842 0.909 0.912 0.869 0.885 0.910 0.882 0.890 0.903 0.906 0.892 0.906 0.899 0.848 0.882 118 94 92 125 129 110 105 112 109 110 121 120 104 126 110 100 116 88 105 112 98 108 130 128 126 108 107 127 112 117 99 107 108 111 112 112 106 123 114 123 94 101 0.921 0.892 0.897 0.934 0.930 0.954 0.934 0.934 0.913 0.911 0.916 0.927 0.889 0.899 0.913 0.920 0.954 0.794 0.921 0.921 0.858 0.891 0.917 0.932 0.918 0.938 0.838 0.831 0.899 0.902 0.859 0.877 0.904 0.874 0.886 0.899 0.902 0.883 0.899 0.892 0.835 0.871 118 94 92 125 129 110 104 112 108 110 120 119 103 124 109 100 116 88 104 111 98 107 129 128 125 107 106 126 111 116 98 107 107 110 111 112 106 121 113 122 93 100 Milwaukee Milwaukee Milwaukee New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City New York City Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Philadelphia Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- RACINE, Wl MILWAUKEE, Wl MILWAUKEE, Wl NEW HAVEN, CT NEW HAVEN, CT BERGEN, NJ NEWARK, NJ JERSEY CITY, NJ MIDDLESEX, NJ TRENTON, NJ MIDDLESEX, NJ MONMOUTH, NJ NEWARK, NJ MONMOUTH, NJ BERGEN, NJ NEWARK, NJ NEW YORK, NY DUTCHESS CO, NY NEW YORK, NY NEW YORK, NY NEWBURGH, NY-PA NEW YORK, NY NEW YORK, NY NEW YORK, NY NASSAU, NY NEW YORK, NY WILMINGTON, DE-MD WILMINGTON, DE-MD Atlantic City, NJ PHILADELPHIA, PA-NJ VINELAND, NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ PHILADELPHIA, PA-NJ WASHINGTON, DC BALTIMORE.MD BALTIMORE.MD BALTIMORE.MD WASHINGTON, DC BALTIMORE.MD IV-6-14 ------- Maryland Maryland Maryland Maryland Maryland Virginia Virginia Virginia Virginia Virginia Virginia Virginia New York New York New York Pennsylvania Pennsylvania Pennsylvania Wisconsin Wisconsin North Carolina Georgia Georgia Georgia Georgia Georgia Georgia Georgia Georgia South Carolina South Carolina Texas Maine Massachusetts Charles Frederick Harford Montgomery Prince Georges Alexandria City Arlington Fairfax Fauquier Loudoun Prince William Stafford Albany Saratoga Schenectady Lehigh Northampton Blair Outagamie Winnebago Buncombe De Kalb Douglas Fayette Fulton Gwinnett Paulding Rockdale Richmond Aiken Edgefield Travis Penobscot Barnstable 123 108 141 117 129 119 119 125 107 116 115 112 105 99 90 114 111 114 94 94 108 133 133 141 146 134 124 134 118 109 111 110 94 124 0.857 0.880 0.881 0.892 0.892 0.909 0.909 0.904 0.871 0.888 0.877 0.879 0.833 0.846 0.815 0.884 0.893 0.851 0.864 0.902 0.825 0.905 0.896 0.883 0.899 0.894 0.849 0.875 0.886 0.861 0.851 0.936 0.942 0.911 105 95 124 104 115 108 108 112 93 102 100 98 87 83 73 100 99 97 81 84 89 120 119 124 131 119 105 117 104 93 94 102 88 112 0.844 0.869 0.871 0.884 0.883 0.906 0.906 0.896 0.860 0.880 0.867 0.867 0.819 0.829 0.808 0.877 0.883 0.839 0.854 0.892 0.803 0.886 0.866 0.850 0.876 0.860 0.826 0.839 0.857 0.841 0.827 0.919 0.932 0.900 103 93 122 103 113 107 107 112 91 102 99 97 86 82 72 99 97 95 80 83 86 117 115 119 127 115 102 112 101 91 91 101 87 111 Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore Washington- Baltimore WASHINGTON, DC WASHINGTON, DC BALTIMORE.MD WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC WASHINGTON, DC ALBANY, NY ALBANY, NY ALBANY, NY ALLENTOWN, PA ALLENTOWN, PA ALTOONA, PA APPLETON, Wl APPLETON, Wl ASHEVILLE, NC ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA ATLANTA, GA AUGUSTA, GA-SC AUGUSTA, GA-SC AUGUSTA, GA-SC AUSTIN, TX BANGOR, ME BARNSTABLE, MA IV-6-15 ------- Louisiana Louisiana Louisiana Louisiana Texas Texas Michigan Mississippi Mississippi Alabama Alabama Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts Massachusetts New Hampshire New York New York Vermont Ohio Iowa Illinois West Virginia South Carolina South Carolina North Carolina North Carolina North Carolina South Carolina Tennessee Kentucky South Carolina Georgia Ohio Ohio Ohio Ohio Texas Illinois Iowa Florida Ohio Ohio Ohio Ascension East Baton Rouge Livingston West Baton Rouge Jefferson Orange Berrien Hancock Jackson Jefferson Shelby Bristol Essex Middlesex Plymouth Suffolk Worcester Rocking ham Erie Niagara Chittenden Stark Linn Champaign Kanawha Berkeley Charleston Lincoln Mecklenburg Rowan York Hamilton Christian Richland Muscogee Delaware Franklin Licking Madison Nueces Rock Island Scott Volusia Clark Greene Miami 123 126 127 119 130 122 125 105 115 126 128 118 113 113 102 95 115 120 107 101 82 108 78 100 111 101 99 105 131 126 108 125 101 113 103 116 109 112 112 102 83 94 94 119 116 110 0.990 0.981 0.988 0.979 0.984 0.987 0.907 0.985 0.998 0.893 0.882 0.883 0.954 0.923 0.909 0.912 0.910 0.959 0.922 0.920 *** 0.904 0.934 0.869 0.891 0.900 0.873 0.872 0.897 0.818 0.874 0.862 0.751 0.896 0.900 0.882 0.897 0.878 0.872 *** 0.924 0.921 0.955 0.877 0.865 0.875 121 123 125 116 127 120 113 103 114 112 112 104 107 104 92 86 104 115 98 92 *** 97 72 86 98 90 86 91 117 103 94 107 75 101 92 102 97 98 97 *** 76 86 89 104 100 96 0.982 0.970 0.981 0.965 0.974 0.979 0.897 0.975 0.988 0.871 0.860 0.872 0.945 0.914 0.897 0.902 0.900 0.951 0.916 0.911 *** 0.890 0.925 0.857 0.882 0.876 0.852 0.851 0.877 0.795 0.852 0.829 0.742 0.864 0.867 0.869 0.894 0.864 0.861 *** 0.920 0.915 0.928 0.863 0.852 0.860 120 122 124 114 126 119 112 102 113 109 110 102 106 103 91 85 103 114 97 91 *** 96 72 85 97 88 84 89 114 100 92 103 74 97 89 100 97 96 96 *** 76 86 87 102 98 94 BATON ROUGE, LA BATON ROUGE, LA BATON ROUGE, LA BATON ROUGE, LA BEAUMONT, TX BEAUMONT, TX BENTON HARBOR, Ml BILOXI, MS BILOXI, MS BIRMINGHAM, AL BIRMINGHAM, AL BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BOSTON, MA-NH BUFFALO, NY BUFFALO, NY BURLINGTON, VT CANTON, OH CEDAR RAPIDS, I A CHAMPAIGN, IL CHARLESTON, WV CHARLESTON, SC CHARLESTON, SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHARLOTTE, NC-SC CHATTANOOGA, TN-GA CLARKSVILLE, TN-KY COLUMBIA, SC COLUMBUS, GA-AL COLUMBUS, OH COLUMBUS, OH COLUMBUS, OH COLUMBUS, OH CORPUS CHRISTI, TX DAVENPORT, IA-IL DAVENPORT, IA-IL DAYTONA BEACH, FL DAYTON, OH DAYTON, OH DAYTON, OH IV-6-16 ------- Ohio Alabama Alabama Illinois Iowa Iowa Delaware Indiana New York Pennsylvania Indiana Indiana Indiana Kentucky North Carolina Florida Florida Indiana Indiana Florida Michigan Michigan Michigan Michigan Wisconsin North Carolina North Carolina North Carolina North Carolina South Carolina South Carolina South Carolina South Carolina Pennsylvania Pennsylvania Connecticut Connecticut Connecticut Connecticut North Carolina North Carolina Louisiana Kentucky Kentucky Kentucky Ohio West Virginia Montgomery Lawrence Morgan Macon Polk Warren Kent Elkhart Chemung Erie Posey Vanderburgh Warrick Henderson Cumberland Lee St Lucie Allen De Kalb Alachua Allegan Kent Muskegon Ottawa Brown Da vie Forsyth Guilford Pitt Anderson Cherokee Pickens Spartanburg Dauphin Perry Hartford Litchfield Middlesex Tolland Alexander Caldwell Lafourche Boyd Carter Greenup Lawrence Cabell 112 101 114 100 76 79 128 113 93 117 105 114 115 108 108 98 88 101 82 105 123 106 121 106 105 113 120 112 109 118 116 109 112 112 103 139 120 135 132 110 111 110 107 118 118 123 129 0.874 0.813 0.876 0.859 0.889 0.881 0.901 0.884 0.900 0.908 0.875 0.873 0.853 0.865 0.905 0.989 0.997 0.904 0.904 0.923 0.917 0.897 0.919 0.919 0.898 0.809 0.862 0.864 0.900 0.870 0.860 0.847 0.853 0.887 0.844 0.923 0.897 0.939 0.919 0.822 0.869 0.987 0.858 0.824 0.842 0.837 0.862 97 82 99 85 67 69 115 99 83 106 91 99 98 93 97 96 87 91 74 96 112 95 111 97 94 91 103 96 98 102 99 92 95 99 86 128 107 126 121 90 96 108 91 97 99 102 111 0.864 0.797 0.861 0.846 0.874 0.867 0.887 0.873 0.884 0.897 0.865 0.862 0.844 0.856 0.878 0.966 0.975 0.890 0.890 0.900 0.911 0.887 0.912 0.913 0.889 0.790 0.836 0.840 0.882 0.846 0.840 0.823 0.829 0.866 0.825 0.917 0.888 0.933 0.910 0.807 0.847 0.980 0.847 0.814 0.832 0.826 0.851 96 80 98 84 66 68 113 98 82 105 90 98 97 92 94 94 85 89 72 94 111 94 110 96 93 89 100 94 96 99 97 89 92 97 85 127 106 126 120 88 93 107 90 96 98 101 109 DAYTON, OH DECATUR, AL DECATUR, AL DECATUR, IL DES MOINES, IA DES MOINES, IA DOVER, DE ELKHART, IN ELMIRA, NY ERIE, PA EVANSVILLE, IN-KY EVANSVILLE, IN-KY EVANSVILLE, IN-KY EVANSVILLE, IN-KY FAYETTEVILLE, NC FORT MEYERS, FL FORT PIERCE, FL FORT WAYNE, IN FORT WAYNE, IN GAINESVILLE, FL GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GRAND RAPIDS-MUSKEGON, Ml GREEN BAY, Wl GREENSBORO, NC GREENSBORO, NC GREENSBORO, NC GREENVILLE, NC GREENVILLE, SC GREENVILLE, SC GREENVILLE, SC GREENVILLE, SC HARRISBURG, PA HARRISBURG, PA HARTFORD, CT HARTFORD, CT HARTFORD, CT HARTFORD, CT HICKORY, NC HICKORY NC HOUMA, LA HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH HUNTINGTON, WV-KY-OH IV-6-17 ------- Alabama Indiana Indiana Indiana Indiana Indiana Indiana Mississippi Mississippi Florida Florida New York Wisconsin Tennessee Pennsylvania Michigan Kansas Kansas Missouri Missouri Missouri Tennessee Tennessee Tennessee Tennessee Tennessee Louisiana Louisiana Florida Pennsylvania Michigan Michigan Kentucky Kentucky Kentucky Ohio Nebraska Arkansas Texas Indiana Indiana Kentucky Kentucky Kentucky New Hampshire Georgia Wisconsin Madison Hamilton Hancock Johnson Madison Marion Morgan Hinds Madison Duval St Johns Chautauqua Rock Sullivan Cambria Kalamazoo Miami Wyandotte Clay Jackson Platte Anderson Blount Knox Loudon Sevier Lafayette Calcasieu Polk Lancaster Clinton Ingham Fayette Jessamine Scott Allen Lancaster Pulaski Gregg Clark Floyd Bullitt Jefferson Oldham Hillsborough Bibb Dane 104 125 120 102 112 118 103 104 101 111 91 106 100 113 112 105 114 113 124 94 122 107 118 134 112 119 101 122 102 121 93 97 101 102 103 102 64 102 128 130 127 111 121 120 110 134 94 0.899 0.900 0.902 0.863 0.901 0.904 0.887 0.944 0.968 0.965 0.953 0.910 0.898 0.861 0.875 0.897 0.944 0.950 0.947 0.933 0.946 0.834 0.840 0.860 0.846 0.796 0.974 0.987 0.970 0.895 0.914 0.916 0.885 0.878 0.831 0.892 0.938 0.952 0.977 0.889 0.895 0.874 0.892 0.871 0.902 0.808 0.921 93 112 108 88 100 106 91 98 97 107 86 96 89 97 98 94 107 107 117 87 115 89 99 115 94 94 98 120 98 108 84 88 89 89 85 91 60 97 125 115 113 96 107 104 99 108 86 0.878 0.889 0.890 0.853 0.888 0.896 0.881 0.921 0.960 0.936 0.925 0.899 0.890 0.847 0.864 0.886 0.933 0.941 0.935 0.923 0.937 0.813 0.816 0.838 0.824 0.778 0.964 0.979 0.946 0.882 0.904 0.907 0.872 0.866 0.819 0.880 0.925 0.927 0.960 0.883 0.891 0.868 0.888 0.864 0.895 0.783 0.909 91 111 106 87 99 105 90 95 96 103 84 95 88 95 96 93 106 106 115 86 114 86 96 112 92 92 97 119 96 106 84 87 88 88 84 89 59 94 122 114 113 96 107 103 98 104 85 HUNTSVILLE, AL INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN INDIANAPOLIS, IN JACKSON, MS JACKSON, MS JACKSONVILLE, FL JACKSONVILLE, FL JAMESTOWN, NY JANESVILLE-BELOIT, Wl JOHNSON CITY, TN-VA JOHNSTOWN, PA KALAMAZOO, Ml KANSAS CITY, MO-KS KANSAS CITY, MO-KS KANSAS CITY, MO-KS KANSAS CITY, MO-KS KANSAS CITY, MO-KS KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN KNOXVILLE, TN LAFAYETTE, LA LAKE CHARLES, LA LAKELAND, FL LANCASTER, PA LANSING, Ml LANSING, Ml LEXINGTON, KY LEXINGTON, KY LEXINGTON, KY LIMA, OH LINCOLN, NE LITTLE ROCK, AR LONGVIEW, TX LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOUISVILLE, KY-IN LOWELL, MA-NH MACON, GA MADISON, Wl IV-6-18 ------- New Hampshire Florida Arkansas Mississippi Tennessee Minnesota Minnesota Minnesota Wisconsin Alabama Louisiana Alabama Alabama Tennessee Tennessee Tennessee Tennessee Tennessee Connecticut Louisiana Louisiana Louisiana Louisiana Louisiana Louisiana Virginia Virginia Florida Oklahoma Oklahoma Oklahoma Nebraska Florida Florida Florida Kentucky Ohio West Virginia Florida Illinois Pennsylvania Pennsylvania Pennsylvania Pennsylvania Massachusetts Maine Maine Merrimack Brevard Crittenden De Soto Shelby Anoka Dakota Washington St Croix Mobile Ouachita Elmore Montgomery Davidson Rutherford Sumner Williamson Wilson New London Jefferson Orleans St Bernard St Charles St James St John The Baptis Hampton City Suffolk City Marion Cleveland Me Clain Oklahoma Douglas Orange Osceola Seminole Daviess Washington Wood Escambia Peoria Allegheny Beaver Washington Westmoreland Berkshire Cumberland York 98 93 118 131 123 93 87 97 88 114 94 109 118 120 101 127 114 108 137 111 92 105 108 108 109 109 108 101 104 98 109 82 109 104 100 108 110 111 117 89 122 113 123 104 108 121 121 0.864 0.986 0.914 0.941 0.943 0.913 0.918 0.948 0.938 0.920 0.976 0.894 0.891 0.901 0.870 0.887 0.813 0.858 0.930 0.970 0.969 0.982 0.978 0.990 0.982 0.912 0.908 0.936 0.972 0.993 0.979 0.928 0.985 0.983 0.975 0.846 0.843 0.830 0.967 0.899 0.895 0.932 0.876 0.897 0.880 0.932 0.932 84 91 107 123 116 84 79 91 82 104 91 97 105 108 87 112 92 92 127 107 89 103 105 106 107 99 98 94 101 97 106 76 107 102 97 91 92 92 113 79 109 105 107 93 95 112 112 0.858 0.960 0.904 0.928 0.935 0.912 0.915 0.938 0.924 0.904 0.965 0.871 0.867 0.882 0.846 0.869 0.795 0.841 0.924 0.962 0.963 0.975 0.972 0.982 0.975 0.904 0.900 0.910 0.952 0.972 0.962 0.917 0.956 0.955 0.945 0.838 0.833 0.821 0.949 0.890 0.885 0.921 0.866 0.887 0.869 0.923 0.923 84 89 106 121 114 84 79 91 81 103 90 94 102 105 85 110 90 90 126 106 88 102 104 106 106 98 97 91 98 95 104 75 104 99 94 90 91 91 111 79 107 104 106 92 93 111 111 MANCHESTER, NH MELBOURNE, FL MEMPHIS, TN-AR-MS MEMPHIS, TN-AR-MS MEMPHIS, TN-AR-MS MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MINNEAPOLIS, MN-WI MOBILE, AL MONROE, LA MONTGOMERY, AL MONTGOMERY, AL NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NASHVILLE, TN NEW LONDON, CT-MA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NEW ORLEANS, LA NORFOLK, VA NORFOLK, VA OCALA, FL OKLAHOMA CITY, OK OKLAHOMA CITY, OK OKLAHOMA CITY, OK OMAHA, NE-IA ORLANDO, FL ORLANDO, FL ORLANDO, FL OWENSBORO, KY PARKERSBURG, WV-OH PARKERSBURG, WV-OH PENSACOLA, FL PEORIA-PEKIN, IL PITTSBURGH, PA PITTSBURGH, PA PITTSBURGH, PA PITTSBURGH, PA PITTSFIELD, MA PORTLAND, ME PORTLAND, ME IV-6-19 ------- New Hampshire Rhode Island Rhode Island Rhode Island North Carolina North Carolina North Carolina North Carolina North Carolina Pennsylvania Virginia Virginia Virginia Virginia Virginia New York New York Illinois North Carolina Florida Florida Georgia Pennsylvania Pennsylvania Pennsylvania Wisconsin Louisiana Louisiana Indiana Illinois Massachusetts Massachusetts Missouri Illinois Illinois Illinois Missouri Missouri Missouri Missouri Pennsylvania Ohio West Virginia New York New York Florida Florida Strafford Kent Providence Washington Chatham Durham Franklin Johnston Wake Berks Charles City Chesterfield Hanover Henri co Roanoke Monroe Wayne Winnebago Edgecombe Manatee Sarasota Chatham Lackawanna Luzerne Mercer Sheboygan Bossier Caddo St Joseph Sangamon Hampden Hampshire Greene Jersey Madison St Clair Jefferson St Charles St Louis St Louis City Centre Jefferson Hancock Madison Onondaga Leon Hillsborough 101 114 108 111 106 110 112 110 116 117 123 114 125 116 110 96 101 86 106 102 106 87 108 110 117 138 109 102 117 96 116 128 94 122 118 98 118 131 119 107 113 94 99 90 95 97 123 0.917 0.918 0.926 0.909 0.856 0.883 0.868 0.890 0.908 0.876 0.872 0.881 0.882 0.892 0.846 0.923 0.927 0.905 0.908 0.974 0.968 0.924 0.853 0.840 0.885 0.927 0.967 0.967 0.896 0.857 0.906 0.897 0.815 0.896 0.922 0.930 0.915 0.922 0.924 0.927 0.861 0.903 0.903 0.938 0.911 0.928 0.973 92 104 100 100 90 97 97 97 105 102 107 100 110 103 93 88 93 77 96 99 102 80 92 92 103 127 105 98 104 82 105 114 76 109 108 91 107 120 109 99 97 84 89 84 86 90 119 0.911 0.911 0.917 0.900 0.835 0.862 0.846 0.866 0.885 0.864 0.859 0.868 0.866 0.877 0.828 0.913 0.918 0.898 0.893 0.952 0.944 0.905 0.838 0.825 0.868 0.923 0.950 0.950 0.886 0.842 0.898 0.890 0.792 0.875 0.902 0.912 0.896 0.900 0.905 0.911 0.845 0.892 0.892 0.925 0.899 0.900 0.960 91 103 99 99 88 94 94 95 102 101 105 98 108 101 91 87 92 77 94 97 100 78 90 90 101 127 103 96 103 80 104 113 74 106 106 89 105 117 107 97 95 83 88 83 85 87 118 PORTSMOUTH, NH-ME PROVIDENCE, RI-MA PROVIDENCE, RI-MA PROVIDENCE, RI-MA RALEIGH, NC RALEIGH, NC RALEIGH, NC RALEIGH, NC RALEIGH, NC READING, PA RICHMOND, VA RICHMOND, VA RICHMOND, VA RICHMOND, VA ROANOKE, VA ROCHESTER, NY ROCHESTER, NY ROCKFORD, IL ROCKY MOUNT, NC SARASOTA, FL SARASOTA, FL SAVANNAH, GA SCRANTON, PA SCRANTON-, PA SHARON, PA SHEBOYGAN, Wl SHREVEPORT, LA SHREVEPORT, LA SOUTH BEND, IN SPRINGFIELD, IL SPRINGFIELD, MA SPRINGFIELD, MA SPRINGFIELD, MO ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL ST. LOUIS, MO-IL STATE COLLEGE, PA STEUBENVILLE, OH-WV STEUBENVILLE, OH-WV SYRACUSE, NY SYRACUSE, NY TALLAHASSEE, FL TAMPA, FL IV-6-20 ------- Florida Florida Indiana Ohio Ohio Oklahoma Texas New York New York Texas Wisconsin Florida West Virginia Kansas Pennsylvania North Carolina Pennsylvania Ohio Ohio Alabama Alabama Alabama Arkansas Arkansas Delaware Georgia Georgia Georgia Georgia Illinois Illinois Illinois Illinois Illinois Indiana Indiana Indiana Iowa Iowa Iowa Iowa Kansas Kentucky Kentucky Kentucky Kentucky Kentucky Pasco Pinellas Vigo Lucas Wood Tulsa Smith Herkimer Oneida Victoria Marathon Palm Beach Ohio Sedgwick Lycoming New Hanover York Ma honing Trumbull Clay Geneva Sumter Montgomery Newton Sussex Dawson Fannin Glynn Sumter Adams Effing ham Hamilton Macoupin Randolph La Porte Lawrence Perry Harrison Palo Alto Story Van Buren Linn Bell Edmonson Graves Hancock Hardin 98 104 107 108 97 116 107 84 91 92 83 102 105 97 95 102 109 111 114 110 88 81 85 84 125 108 96 99 98 98 96 89 107 97 128 100 114 92 81 87 82 104 98 108 102 111 96 0.965 0.962 0.889 0.920 0.914 0.977 0.955 0.954 0.906 *** 0.893 0.979 0.869 0.961 0.875 0.921 0.889 0.891 0.890 0.817 0.893 0.813 *** *** 0.880 0.862 0.855 0.920 0.881 0.895 0.838 0.830 0.856 0.841 0.924 0.823 0.820 0.924 *** 0.887 0.899 0.961 0.795 0.792 0.861 0.820 0.827 94 100 95 99 88 113 102 80 82 *** 74 99 91 93 83 93 96 98 101 89 78 65 *** *** 109 93 82 91 86 87 80 73 91 81 118 82 93 85 *** 77 73 99 77 85 87 91 79 0.942 0.954 0.878 0.914 0.905 0.965 0.939 0.949 0.892 *** 0.883 0.947 0.858 0.948 0.861 0.905 0.875 0.875 0.875 0.797 0.869 0.801 *** *** 0.867 0.832 0.823 0.901 0.856 0.887 0.825 0.821 0.837 0.830 0.919 0.811 0.812 0.913 *** 0.873 0.890 0.949 0.776 0.781 0.853 0.811 0.818 92 99 93 98 87 111 100 79 81 *** 73 96 90 91 81 92 95 97 99 87 76 64 *** *** 108 89 78 89 83 86 79 73 89 80 117 81 92 84 *** 75 72 98 76 84 86 90 78 TAMPA, FL TAMPA, FL TERRE HAUTE, IN TOLEDO, OH TOLEDO, OH TULSA, OK TYLER, TX UTICA-ROME, NY UTICA-ROME, NY VICTORIA, TX WAUSAU, Wl WEST PALM BEACH, FL WHEELING, WV-OH WICHITA, KS WILLIAMSPORT, PA WILMINGTON, NC YORK, PA YOUNGSTOWN, OH YOUNGSTOWN, OH IV-6-21 ------- Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Kentucky Louisiana Louisiana Louisiana Louisiana Louisiana Maine Maine Maine Maine Maine Maine Maine Maryland Michigan Michigan Michigan Michigan Michigan Michigan Michigan Mississippi Mississippi Mississippi Mississippi Mississippi Mississippi Mississippi Missouri Missouri Missouri New Hampshire New Hampshire New Hampshire New Hampshire New Hampshire New Hampshire New York New York Lawrence Livingston McCracken McLean Perry Pike Pulaski Simpson Trigg Beauregard Grant Iberville Pointe Coupee St Mary Hancock Kennebec Knox Oxford Piscataquis Sagadahoc Somerset Kent Benzie Cass Huron Mason Mecosta Missaukee Roscommon Adams Choctaw Lauderdale Lee Panola Sharkey Warren Cedar Monroe Ste Genevieve Belknap Carroll Cheshire Coos Grafton Sullivan Essex Hamilton 95 113 109 106 90 100 95 113 106 109 95 126 111 102 118 102 113 77 68 124 93 126 107 115 106 123 124 97 99 97 81 92 107 119 95 97 96 97 106 88 79 91 101 84 93 93 91 0.798 0.841 0.855 0.856 0.766 0.782 0.832 0.817 0.805 0.978 0.974 0.990 0.976 0.990 0.916 0.950 0.916 *** *** 0.933 0.958 0.874 0.933 0.890 0.931 0.919 0.911 0.912 0.916 0.968 0.858 0.848 0.828 0.887 0.954 0.977 0.946 0.870 0.875 0.814 0.858 0.921 0.847 0.831 0.906 0.951 0.813 75 95 93 90 68 78 79 92 85 106 92 124 108 100 108 96 103 *** *** 115 89 110 99 102 98 113 112 88 90 93 69 78 88 105 90 94 90 84 92 71 67 83 85 69 84 88 74 0.789 0.833 0.847 0.847 0.754 0.768 0.818 0.805 0.796 0.969 0.961 0.983 0.963 0.984 0.906 0.942 0.906 *** *** 0.924 0.946 0.862 0.924 0.879 0.923 0.912 0.901 0.902 0.907 0.957 0.842 0.828 0.812 0.873 0.945 0.968 0.937 0.859 0.860 0.809 0.855 0.910 0.842 0.824 0.897 0.945 0.804 74 94 92 89 67 76 77 90 84 105 91 123 106 100 106 96 102 *** *** 114 87 108 98 101 97 112 111 87 89 92 68 76 86 103 89 93 89 83 91 71 67 82 85 69 83 87 73 IV-6-22 ------- New York New York North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina North Carolina Ohio Ohio Ohio Ohio Ohio Oklahoma Oklahoma Oklahoma Oklahoma Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania Pennsylvania South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina South Carolina Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Tennessee Jefferson Ulster Avery Camden Caswell Duplin Granville Haywood Lenoir Martin Northampton Person Rocking ham Swain Yancey Clinton Knox Logan Preble Union Latimer Mayes Muskogee Okmulgee Armstrong Clearfield Franklin Greene Lawrence Monroe Abbeville Barnwell Chester Colleton Darlington Oconee Union Williamsburg Bradley Coffee Giles Hamblen Haywood Humphreys Jefferson Lawrence Putnam 104 96 96 93 118 99 124 106 109 94 103 117 123 87 94 118 108 99 111 96 108 106 93 104 113 117 115 123 98 116 103 108 113 99 99 103 99 89 106 96 104 96 120 102 126 100 106 0.928 0.834 0.884 0.920 0.854 0.862 0.875 0.827 0.879 0.913 0.846 0.871 0.860 0.790 0.867 0.846 0.893 0.886 0.867 0.875 0.960 0.974 0.987 0.975 0.890 0.872 0.835 0.796 0.901 0.881 0.883 0.838 0.878 0.862 0.886 0.838 0.816 0.823 0.771 0.836 0.752 0.798 0.881 0.777 0.803 0.713 0.822 96 80 84 85 100 85 108 87 95 85 87 101 105 68 81 99 96 87 96 84 103 103 91 101 100 102 95 97 88 102 90 90 99 85 87 86 80 73 81 80 78 76 105 79 101 71 87 0.922 0.825 0.862 0.910 0.835 0.843 0.854 0.804 0.860 0.898 0.832 0.853 0.837 0.771 0.845 0.833 0.881 0.872 0.852 0.862 0.949 0.962 0.972 0.961 0.879 0.860 0.820 0.785 0.885 0.873 0.862 0.819 0.856 0.843 0.861 0.809 0.797 0.808 0.751 0.817 0.737 0.784 0.869 0.769 0.785 0.699 0.804 95 79 82 84 98 83 105 85 93 84 85 99 103 67 79 98 95 86 94 82 102 102 90 99 99 100 94 96 86 101 88 88 96 83 85 83 78 71 79 78 76 75 104 78 98 69 85 IV-6-23 ------- Texas Vermont Virginia Virginia Virginia Virginia Virginia West Virginia Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Wisconsin Marion Bennington Caroline Frederick Henry Madison Wythe Greenbrier Columbia Dodge Florence Fond Du Lac Jefferson Kewaunee Manitowoc Oneida Polk Sauk Vernon Walworth 94 98 111 109 101 115 96 111 91 100 83 96 93 117 158 82 81 89 83 100 0.973 0.911 0.873 0.824 0.813 0.790 0.774 0.731 0.918 0.877 *** 0.886 0.893 0.920 0.919 *** 0.929 0.883 0.920 0.894 91 89 96 89 82 90 74 81 83 87 *** 85 83 107 145 *** 75 78 76 89 0.955 0.901 0.858 0.811 0.796 0.777 0.760 0.719 0.906 0.869 *** 0.878 0.884 0.911 0.915 *** 0.915 0.872 0.909 0.888 89 88 95 88 80 89 72 79 82 86 *** 84 82 106 144 *** 74 77 75 88 IV-6-24 ------- |